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Kitamura FC, Prevedello LM, Colak E, Halabi SS, Lungren MP, Ball RL, Kalpathy-Cramer J, Kahn CE, Richards T, Talbott JF, Shih G, Lin HM, Andriole KP, Vazirabad M, Erickson BJ, Flanders AE, Mongan J. Lessons Learned in Building Expertly Annotated Multi-Institution Datasets and Hosting the RSNA AI Challenges. Radiol Artif Intell 2024; 6:e230227. [PMID: 38477659 DOI: 10.1148/ryai.230227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/14/2024]
Abstract
The Radiological Society of North America (RSNA) has held artificial intelligence competitions to tackle real-world medical imaging problems at least annually since 2017. This article examines the challenges and processes involved in organizing these competitions, with a specific emphasis on the creation and curation of high-quality datasets. The collection of diverse and representative medical imaging data involves dealing with issues of patient privacy and data security. Furthermore, ensuring quality and consistency in data, which includes expert labeling and accounting for various patient and imaging characteristics, necessitates substantial planning and resources. Overcoming these obstacles requires meticulous project management and adherence to strict timelines. The article also highlights the potential of crowdsourced annotation to progress medical imaging research. Through the RSNA competitions, an effective global engagement has been realized, resulting in innovative solutions to complex medical imaging problems, thus potentially transforming health care by enhancing diagnostic accuracy and patient outcomes. Keywords: Use of AI in Education, Artificial Intelligence © RSNA, 2024.
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Affiliation(s)
- Felipe C Kitamura
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Luciano M Prevedello
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Errol Colak
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Safwan S Halabi
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Matthew P Lungren
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Robyn L Ball
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Jayashree Kalpathy-Cramer
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Charles E Kahn
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Tyler Richards
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Jason F Talbott
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - George Shih
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Hui Ming Lin
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Katherine P Andriole
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Maryam Vazirabad
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Bradley J Erickson
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - Adam E Flanders
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
| | - John Mongan
- From the Department of Applied Innovation and AI, Dasa, São Paulo, Brazil (F.C.K.); Department of Diagnostic Imaging, Universidade Federal de São Paulo (Unifesp), Av Prof Ascendino Reis, 1245, 131, São Paulo, SP, Brazil 04027-000 (F.C.K.); Department of Radiology, The Ohio State University Wexner Medical Center, Columbus, Ohio (L.M.P.); Department of Medical Imaging, University of Toronto, Toronto, Canada (E.C.); Ann and Robert H. Lurie Children's Hospital of Chicago, Chicago, Ill (S.S.H.); Microsoft HLS, Redmond, Wash (M.P.L.); Department of Biomedical Data Science, Stanford University, Stanford, Calif (M.P.L.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of Ophthalmology, University of Colorado Denver School of Medicine, Aurora, Colo (J.K.C.); Department of Radiology, University of Pennsylvania, Philadelphia, Pa (C.E.K.); Department of Radiology, University of Utah, Salt Lake City, Utah (T.R.); Department of Radiology and Biomedical Imaging (M.P.L., J.F.T., J.M.) and Center for Intelligent Imaging (J.M.), University of California San Francisco, San Francisco, Calif; Department of Radiology, Weill Cornell Medical College, New York, NY (G.S.); Department of Medical Imaging, Unity Health Toronto, Toronto, Canada (H.M.L.); Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, MGB Data Science Office, Boston, Mass (K.P.A.); Informatics Department, Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Mayo Clinic, Rochester, Minn (B.J.E.); and Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.)
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Lorenzo G, Ahmed SR, Hormuth Ii DA, Vaughn B, Kalpathy-Cramer J, Solorio L, Yankeelov TE, Gomez H. Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data. Annu Rev Biomed Eng 2024. [PMID: 38594947 DOI: 10.1146/annurev-bioeng-081623-025834] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/11/2024]
Abstract
Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.
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Affiliation(s)
- Guillermo Lorenzo
- 1Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy
- 2Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
| | - Syed Rakin Ahmed
- 3Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA
- 4Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, Massachusetts, USA
- 5Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
- 6Geisel School of Medicine, Dartmouth College, Hanover, New Hampshire, USA
| | - David A Hormuth Ii
- 2Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
- 7Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
| | - Brenna Vaughn
- 8Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | | | - Luis Solorio
- 8Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
| | - Thomas E Yankeelov
- 10Department of Biomedical Engineering, Department of Oncology, and Department of Diagnostic Medicine, University of Texas, Austin, Texas, USA
- 11Department of Imaging Physics, MD Anderson Cancer Center, Houston, Texas, USA
- 2Oden Institute for Computational Engineering and Sciences, University of Texas, Austin, Texas, USA
- 7Livestrong Cancer Institutes, University of Texas, Austin, Texas, USA
| | - Hector Gomez
- 12School of Mechanical Engineering and Purdue Center for Cancer Research, Purdue University, West Lafayette, Indiana, USA
- 8Weldon School of Biomedical Engineering, Purdue University, West Lafayette, Indiana, USA;
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Lim-Fat MJ, Iorgulescu JB, Rahman R, Bhave V, Muzikansky A, Woodward E, Whorral S, Allen M, Touat M, Li X, Xy G, Patel J, Gerstner ER, Kalpathy-Cramer J, Youssef G, Chukwueke U, McFaline-Figueroa JR, Nayak L, Lee EQ, Reardon DA, Beroukhim R, Huang RY, Bi WL, Ligon KL, Wen PY. Clinical and Genomic Predictors of Adverse Events in Newly Diagnosed Glioblastoma. Clin Cancer Res 2024; 30:1327-1337. [PMID: 38252427 DOI: 10.1158/1078-0432.ccr-23-3018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/01/2023] [Accepted: 01/18/2024] [Indexed: 01/23/2024]
Abstract
PURPOSE Adverse clinical events cause significant morbidity in patients with GBM (GBM). We examined whether genomic alterations were associated with AE (AE) in patients with GBM. EXPERIMENTAL DESIGN We identified adults with histologically confirmed IDH-wild-type GBM with targeted next-generation sequencing (OncoPanel) at Dana Farber Cancer Institute from 2013 to 2019. Seizure at presentation, lymphopenia, thromboembolic events, pseudoprogression, and early progression (within 6 months of diagnosis) were identified as AE. The biologic function of genetic variants was categorized as loss-of-function (LoF), no change in function, or gain-of-function (GoF) using a somatic tumor mutation knowledge base (OncoKB) and consensus protein function predictions. Associations between functional genomic alterations and AE were examined using univariate logistic regressions and multivariable regressions adjusted for additional clinical predictors. RESULTS Our study included 470 patients diagnosed with GBM who met the study criteria. We focused on 105 genes that had sequencing data available for ≥ 90% of the patients and were altered in ≥10% of the cohort. Following false-discovery rate (FDR) correction and multivariable adjustment, the TP53, RB1, IGF1R, and DIS3 LoF alterations were associated with lower odds of seizures, while EGFR, SMARCA4, GNA11, BRD4, and TCF3 GoF and SETD2 LoF alterations were associated with higher odds of seizures. For all other AE of interest, no significant associations were found with genomic alterations following FDR correction. CONCLUSIONS Genomic biomarkers based on functional variant analysis of a routine clinical panel may help identify AE in GBM, particularly seizures. Identifying these risk factors could improve the management of patients through better supportive care and consideration of prophylactic therapies.
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Affiliation(s)
- Mary Jane Lim-Fat
- Division of Neurology, Department of Medicine, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada
| | - J Bryan Iorgulescu
- Molecular Diagnostics Laboratory, Department of Hematopathology, Division of Pathology and Laboratory Medicine, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Rifaquat Rahman
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Varun Bhave
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Alona Muzikansky
- Massachusetts General Hospital Cancer Center and Harvard Medical School, Boston, Massachusetts
| | - Eleanor Woodward
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Sydney Whorral
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Marie Allen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Mehdi Touat
- Sorbonne Université, Inserm, CNRS, UMR S 1127, Institut du Cerveau et de la Moelle épinière, ICM, AP-HP, Hôpitaux Universitaires La Pitié Salpêtrière - Charles Foix, Service de Neurologie 2-Mazarin, Paris, France
| | | | | | - Jay Patel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Elizabeth R Gerstner
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Gilbert Youssef
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Ugonma Chukwueke
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - J Ricardo McFaline-Figueroa
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Lakshmi Nayak
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Eudocia Q Lee
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - David A Reardon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Rameen Beroukhim
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Raymond Y Huang
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Wenya Linda Bi
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Keith L Ligon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts
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4
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Coyner AS, Murickan T, Oh MA, Young BK, Ostmo SR, Singh P, Chan RVP, Moshfeghi DM, Shah PK, Venkatapathy N, Chiang MF, Kalpathy-Cramer J, Campbell JP. Multinational External Validation of Autonomous Retinopathy of Prematurity Screening. JAMA Ophthalmol 2024; 142:327-335. [PMID: 38451496 PMCID: PMC10921347 DOI: 10.1001/jamaophthalmol.2024.0045] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/29/2023] [Accepted: 12/15/2023] [Indexed: 03/08/2024]
Abstract
Importance Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. Objective To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. Design, Setting, and Participants This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. Exposures An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. Main Outcomes and Measures The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. Results The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis. Conclusions and Relevance Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.
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Affiliation(s)
- Aaron S. Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Tom Murickan
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Minn A. Oh
- Casey Eye Institute, Oregon Health & Science University, Portland
| | | | - Susan R. Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Praveer Singh
- Ophthalmology, University of Colorado School of Medicine, Aurora
| | - R. V. Paul Chan
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago
| | - Darius M. Moshfeghi
- Byers Eye Institute, Department of Ophthalmology, Stanford University School of Medicine, Palo Alto, California
| | - Parag K. Shah
- Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | | | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland
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5
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Hoebel KV, Bridge CP, Kim A, Gerstner ER, Ly IK, Deng F, DeSalvo MN, Dietrich J, Huang R, Huang SY, Pomerantz SR, Vagvala S, Rosen BR, Kalpathy-Cramer J. Not without Context-A Multiple Methods Study on Evaluation and Correction of Automated Brain Tumor Segmentations by Experts. Acad Radiol 2024; 31:1572-1582. [PMID: 37951777 DOI: 10.1016/j.acra.2023.10.019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 10/06/2023] [Accepted: 10/07/2023] [Indexed: 11/14/2023]
Abstract
RATIONALE AND OBJECTIVES Brain tumor segmentations are integral to the clinical management of patients with glioblastoma, the deadliest primary brain tumor in adults. The manual delineation of tumors is time-consuming and highly provider-dependent. These two problems must be addressed by introducing automated, deep-learning-based segmentation tools. This study aimed to identify criteria experts use to evaluate the quality of automatically generated segmentations and their thought processes as they correct them. MATERIALS AND METHODS Multiple methods were used to develop a detailed understanding of the complex factors that shape experts' perception of segmentation quality and their thought processes in correcting proposed segmentations. Data from a questionnaire and semistructured interview with neuro-oncologists and neuroradiologists were collected between August and December 2021 and analyzed using a combined deductive and inductive approach. RESULTS Brain tumors are highly complex and ambiguous segmentation targets. Therefore, physicians rely heavily on the given context related to the patient and clinical context in evaluating the quality and need to correct brain tumor segmentation. Most importantly, the intended clinical application determines the segmentation quality criteria and editing decisions. Physicians' personal beliefs and preferences about the capabilities of AI algorithms and whether questionable areas should not be included are additional criteria influencing the perception of segmentation quality and appearance of an edited segmentation. CONCLUSION Our findings on experts' perceptions of segmentation quality will allow the design of improved frameworks for expert-centered evaluation of brain tumor segmentation models. In particular, the knowledge presented here can inspire the development of brain tumor-specific metrics for segmentation model training and evaluation.
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Affiliation(s)
- Katharina V Hoebel
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Christopher P Bridge
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Albert Kim
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Elizabeth R Gerstner
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Ina K Ly
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Stephen E. and Catherine Pappas Center for Neuro-Oncology, Massachusetts General Hospital, Boston, Massachusetts
| | - Francis Deng
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Massachusetts
| | - Matthew N DeSalvo
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Jorg Dietrich
- Department of Neurology, Division of Neuro-Oncology, Massachusetts General Hospital Cancer Center, Harvard Medical School, Boston, Massachusetts
| | - Raymond Huang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Susie Y Huang
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Stuart R Pomerantz
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Saivenkat Vagvala
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts
| | - Bruce R Rosen
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts; Department of Ophthalmology, University of Colorado Anschutz Medical Campus, 1675 Aurora Court, Mail Stop F731, Aurora, CO.
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6
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Lin WC, Jordan BK, Scottoline B, Ostmo SR, Coyner AS, Singh P, Kalpathy-Cramer J, Erdogmus D, Chan RP, Chiang MF, Campbell JP. Oxygenation Fluctuations Associated with Severe Retinopathy of Prematurity: Insights from a Multimodal Deep Learning Approach. Ophthalmol Sci 2024; 4:100417. [PMID: 38059124 PMCID: PMC10696464 DOI: 10.1016/j.xops.2023.100417] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Revised: 09/27/2023] [Accepted: 10/18/2023] [Indexed: 12/08/2023]
Abstract
Purpose Retinopathy of prematurity (ROP) is one of the leading causes of blindness in children. Although the role of oxygen in the pathophysiology of ROP is well established, a precise understanding of the dynamic relationship between oxygen exposure ROP incidence and severity is lacking. The purpose of this study was to evaluate the correlation between time-dependent oxygen variables and the onset of ROP. Design Retrospective cohort study. Participants Two hundred thirty infants who were born at a single academic center and met the inclusion criteria were included. Infants are mainly born between January 2011 and October 2022. Methods Patient data were extracted from electronic health records (EHRs), with sufficient time-dependent oxygen data. Clinical outcomes for ROP were recorded as none/mild or moderate/severe (defined as type II or worse). Mixed-effects linear models were used to compare the 2 groups in terms of dynamic oxygen variables, such as daily average and the coefficient of variation (COV) fraction of inspired oxygen (FiO2). Support vector machine (SVM) and long-short-term memory (LSTM)-based multimodal models were trained with fivefold cross-validation to predict which infants would develop moderate/severe ROP. Gestational age (GA), birth weight, and time-dependent oxygen variables were used to develop predictive models. Main Outcome Measures Model cross-validation performance was evaluated by computing the mean area under the receiver operating characteristic (AUROC) curve, precision, recall, and F1 score. Results We found that both daily average and COV of FiO2 were associated with more severe ROP (adjusted P < 0.001). With fivefold cross-validation, the multimodal LSTM models had higher performance than the best static models (SVM using GA and 3 average FiO2 features) and SVM models trained on GA alone (mean AUROC = 0.89 ± 0.04 vs. 0.86 ± 0.05 vs. 0.83 ± 0.04). Conclusions The development of severe ROP might not only be influenced by oxygen exposure but also by its fluctuation, which provides direction for future study of pathophysiological factors associated with severe ROP development. Additionally, we demonstrated that multimodal neural networks can be a method to extract useful information from time-series data, which may be a valuable methodology for the investigation of other diseases using EHR data. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Wei-Chun Lin
- Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Brian K. Jordan
- Department of Neonatology, Oregon Health and Science University, Portland, Oregon
| | - Brian Scottoline
- Department of Neonatology, Oregon Health and Science University, Portland, Oregon
| | - Susan R. Ostmo
- Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Aaron S. Coyner
- Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Praveer Singh
- Department of Ophthalmology, University of Colorado (CU) School of Medicine, Denver, Colorado
| | | | - Deniz Erdogmus
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - R.V. Paul Chan
- Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, Illinois
| | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - J. Peter Campbell
- Department of Ophthalmology, Oregon Health & Science University, Portland, Oregon
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7
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de Sanjosé S, Perkins RB, Campos N, Inturrisi F, Egemen D, Befano B, Rodriguez AC, Jerónimo J, Cheung LC, Desai K, Han P, Novetsky AP, Ukwuani A, Marcus J, Ahmed SR, Wentzensen N, Kalpathy-Cramer J, Schiffman M. Design of the HPV-automated visual evaluation (PAVE) study: Validating a novel cervical screening strategy. eLife 2024; 12:RP91469. [PMID: 38224340 PMCID: PMC10945624 DOI: 10.7554/elife.91469] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/16/2024] Open
Abstract
Background The HPV-automated visual evaluation (PAVE) Study is an extensive, multinational initiative designed to advance cervical cancer prevention in resource-constrained regions. Cervical cancer disproportionally affects regions with limited access to preventive measures. PAVE aims to assess a novel screening-triage-treatment strategy integrating self-sampled HPV testing, deep-learning-based automated visual evaluation (AVE), and targeted therapies. Methods Phase 1 efficacy involves screening up to 100,000 women aged 25-49 across nine countries, using self-collected vaginal samples for hierarchical HPV evaluation: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68 else negative. HPV-positive individuals undergo further evaluation, including pelvic exams, cervical imaging, and biopsies. AVE algorithms analyze images, assigning risk scores for precancer, validated against histologic high-grade precancer. Phase 1, however, does not integrate AVE results into patient management, contrasting them with local standard care.Phase 2 effectiveness focuses on deploying AVE software and HPV genotype data in real-time clinical decision-making, evaluating feasibility, acceptability, cost-effectiveness, and health communication of the PAVE strategy in practice. Results Currently, sites have commenced fieldwork, and conclusive results are pending. Conclusions The study aspires to validate a screen-triage-treat protocol utilizing innovative biomarkers to deliver an accurate, feasible, and cost-effective strategy for cervical cancer prevention in resource-limited areas. Should the study validate PAVE, its broader implementation could be recommended, potentially expanding cervical cancer prevention worldwide. Funding The consortial sites are responsible for their own study costs. Research equipment and supplies, and the NCI-affiliated staff are funded by the National Cancer Institute Intramural Research Program including supplemental funding from the Cancer Cures Moonshot Initiative. No commercial support was obtained. Brian Befano was supported by NCI/ NIH under Grant T32CA09168.
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Affiliation(s)
- Silvia de Sanjosé
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
- ISGlobalBarcelonaSpain
| | - Rebecca B Perkins
- University Chobanian and Avedisian School of Medicine/Boston Medical CenterBostonUnited States
| | - Nicole Campos
- Center for Health Decision Science, Harvard T.H. Chan School of Public HealthBostonUnited States
| | - Federica Inturrisi
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Didem Egemen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Brian Befano
- Information Management Services IncCalvertonUnited States
- Department of Epidemiology, University of Washington School of Public HealthSeattleUnited States
| | - Ana Cecilia Rodriguez
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Jose Jerónimo
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Li C Cheung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Kanan Desai
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Paul Han
- Division of Cancer Control and Population Sciences, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Akiva P Novetsky
- Westchester Medical Center/New York Medical CollegeValhallaUnited States
| | - Abigail Ukwuani
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Jenna Marcus
- Feinberg School of Medicine at Northwestern UniversityChicagoUnited States
| | - Syed Rakin Ahmed
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalBostonUnited States
- Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard UniversityCambridgeUnited States
- Massachusetts Institute of TechnologyCambridgeUnited States
- Geisel School of Medicine at Dartmouth, Dartmouth CollegeHanoverUnited States
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General HospitalBostonUnited States
- University of Colorado Anschutz Medical CampusAuroraUnited States
| | - Mark Schiffman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of HealthRockvilleUnited States
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8
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Egemen D, Perkins RB, Cheung LC, Befano B, Rodriguez AC, Desai K, Lemay A, Ahmed SR, Antani S, Jeronimo J, Wentzensen N, Kalpathy-Cramer J, De Sanjose S, Schiffman M. Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening. J Natl Cancer Inst 2024; 116:26-33. [PMID: 37758250 PMCID: PMC10777665 DOI: 10.1093/jnci/djad202] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2023] [Revised: 09/11/2023] [Accepted: 09/21/2023] [Indexed: 10/03/2023] Open
Abstract
Novel screening and diagnostic tests based on artificial intelligence (AI) image recognition algorithms are proliferating. Some initial reports claim outstanding accuracy followed by disappointing lack of confirmation, including our own early work on cervical screening. This is a presentation of lessons learned, organized as a conceptual step-by-step approach to bridge the gap between the creation of an AI algorithm and clinical efficacy. The first fundamental principle is specifying rigorously what the algorithm is designed to identify and what the test is intended to measure (eg, screening, diagnostic, or prognostic). Second, designing the AI algorithm to minimize the most clinically important errors. For example, many equivocal cervical images cannot yet be labeled because the borderline between cases and controls is blurred. To avoid a misclassified case-control dichotomy, we have isolated the equivocal cases and formally included an intermediate, indeterminate class (severity order of classes: case>indeterminate>control). The third principle is evaluating AI algorithms like any other test, using clinical epidemiologic criteria. Repeatability of the algorithm at the borderline, for indeterminate images, has proven extremely informative. Distinguishing between internal and external validation is also essential. Linking the AI algorithm results to clinical risk estimation is the fourth principle. Absolute risk (not relative) is the critical metric for translating a test result into clinical use. Finally, generating risk-based guidelines for clinical use that match local resources and priorities is the last principle in our approach. We are particularly interested in applications to lower-resource settings to address health disparities. We note that similar principles apply to other domains of AI-based image analysis for medical diagnostic testing.
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Affiliation(s)
- Didem Egemen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Rebecca B Perkins
- Department of Obstetrics and Gynecology, Boston Medical Center/Boston University School of Medicine, Boston, MA, USA
| | - Li C Cheung
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Brian Befano
- Information Management Services Inc, Calverton, MD, USA
- Department of Epidemiology, School of Public Health, University of Washington, Seattle, WA, USA
| | - Ana Cecilia Rodriguez
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Kanan Desai
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Andreanne Lemay
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Syed Rakin Ahmed
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
- Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, USA
| | - Sameer Antani
- National Library of Medicine, National Institutes of Health, Bethesda, MD, USA
| | - Jose Jeronimo
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Nicolas Wentzensen
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Silvia De Sanjose
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
- ISGlobal, Barcelona, Spain
| | - Mark Schiffman
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Rockville, MD, USA
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9
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Nguyen TTP, Young BK, Coyner A, Ostmo S, Chan RVP, Kalpathy-Cramer J, Chiang MF, Campbell JP. Discrepancies in Diagnosis of Treatment-Requiring Retinopathy of Prematurity. Ophthalmol Retina 2024; 8:88-91. [PMID: 37689182 PMCID: PMC10841666 DOI: 10.1016/j.oret.2023.09.001] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2023] [Revised: 08/21/2023] [Accepted: 09/01/2023] [Indexed: 09/11/2023]
Abstract
52% of treated eyes with retinopathy of prematurity in a multicenter cohort didn’t require intervention per evaluation by an independent reading center. An artificial intelligence system detected worse vascular severity in the group designed as treatment-requiring by reading center.
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Affiliation(s)
- Thanh-Tin P Nguyen
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Benjamin K Young
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Aaron Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon; Department of Biomedical Engineering, Oregon Health & Science University, Portland, Oregon
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - R V Paul Chan
- Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | | | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland; National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - J Peter Campbell
- Casey Eye Institute, Oregon Health & Science University, Portland, Oregon.
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10
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Li W, Partridge SC, Newitt DC, Steingrimsson J, Marques HS, Bolan PJ, Hirano M, Bearce BA, Kalpathy-Cramer J, Boss MA, Teng X, Zhang J, Cai J, Kontos D, Cohen EA, Mankowski WC, Liu M, Ha R, Pellicer-Valero OJ, Maier-Hein K, Rabinovici-Cohen S, Tlusty T, Ozery-Flato M, Parekh VS, Jacobs MA, Yan R, Sung K, Kazerouni AS, DiCarlo JC, Yankeelov TE, Chenevert TL, Hylton NM. Breast Multiparametric MRI for Prediction of Neoadjuvant Chemotherapy Response in Breast Cancer: The BMMR2 Challenge. Radiol Imaging Cancer 2024; 6:e230033. [PMID: 38180338 PMCID: PMC10825718 DOI: 10.1148/rycan.230033] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Revised: 09/13/2023] [Accepted: 11/02/2023] [Indexed: 01/06/2024]
Abstract
Purpose To describe the design, conduct, and results of the Breast Multiparametric MRI for prediction of neoadjuvant chemotherapy Response (BMMR2) challenge. Materials and Methods The BMMR2 computational challenge opened on May 28, 2021, and closed on December 21, 2021. The goal of the challenge was to identify image-based markers derived from multiparametric breast MRI, including diffusion-weighted imaging (DWI) and dynamic contrast-enhanced (DCE) MRI, along with clinical data for predicting pathologic complete response (pCR) following neoadjuvant treatment. Data included 573 breast MRI studies from 191 women (mean age [±SD], 48.9 years ± 10.56) in the I-SPY 2/American College of Radiology Imaging Network (ACRIN) 6698 trial (ClinicalTrials.gov: NCT01042379). The challenge cohort was split into training (60%) and test (40%) sets, with teams blinded to test set pCR outcomes. Prediction performance was evaluated by area under the receiver operating characteristic curve (AUC) and compared with the benchmark established from the ACRIN 6698 primary analysis. Results Eight teams submitted final predictions. Entries from three teams had point estimators of AUC that were higher than the benchmark performance (AUC, 0.782 [95% CI: 0.670, 0.893], with AUCs of 0.803 [95% CI: 0.702, 0.904], 0.838 [95% CI: 0.748, 0.928], and 0.840 [95% CI: 0.748, 0.932]). A variety of approaches were used, ranging from extraction of individual features to deep learning and artificial intelligence methods, incorporating DCE and DWI alone or in combination. Conclusion The BMMR2 challenge identified several models with high predictive performance, which may further expand the value of multiparametric breast MRI as an early marker of treatment response. Clinical trial registration no. NCT01042379 Keywords: MRI, Breast, Tumor Response Supplemental material is available for this article. © RSNA, 2024.
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Affiliation(s)
- Wen Li
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Savannah C. Partridge
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - David C. Newitt
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Jon Steingrimsson
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Helga S. Marques
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Patrick J. Bolan
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michael Hirano
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Benjamin Aaron Bearce
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Jayashree Kalpathy-Cramer
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michael A. Boss
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Xinzhi Teng
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Jiang Zhang
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Jing Cai
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Despina Kontos
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Eric A. Cohen
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Walter C. Mankowski
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michael Liu
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Richard Ha
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Oscar J. Pellicer-Valero
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Klaus Maier-Hein
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Simona Rabinovici-Cohen
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Tal Tlusty
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michal Ozery-Flato
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Vishwa S. Parekh
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Michael A. Jacobs
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Ran Yan
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Kyunghyun Sung
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Anum S. Kazerouni
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Julie C. DiCarlo
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Thomas E. Yankeelov
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Thomas L. Chenevert
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
| | - Nola M. Hylton
- From the Department of Radiology & Biomedical Imaging,
University of California San Francisco, San Francisco, Calif (W.L., D.C.N.,
N.M.H.); Department of Radiology, University of Washington, Fred Hutchinson
Cancer Center, 1100 Fairview Ave N, Seattle, WA 98109 (S.C.P., M.H., A.S.K.);
Center for Statistical Sciences, Brown University, Providence, RI (J.S.,
H.S.M.); Center for Magnetic Resonance Research, University of Minnesota,
Minneapolis, Minn (P.J.B.); Athinoula A. Martinos Center for Biomedical Imaging,
Harvard University, Charlestown, Mass (B.A.B., J.K.C.); Center for Research and
Innovation, American College of Radiology, Philadelphia, Pa (M.A.B.); Department
of Health Technology and Informatics, The Hong Kong Polytechnic University, Hung
Hom, Kowloon, Hong Kong SAR (X.T., J.Z., J.C.); Department of Radiology,
University of Pennsylvania, Philadelphia, Pa (D.K., E.A.C., W.C.M.); Department
of Radiology, Columbia University Medical Center, New York, NY (M.L., R.H.);
Division of Medical Image Computing, German Cancer Research Center, Heidelberg,
Germany (O.J.P.V., K.M.H.); Department of Radiation Oncology, Heidelberg
University Hospital, Heidelberg, Germany (K.M.H.); IBM Research-Israel, Haifa
University Campus, Mount Carmel, Haifa, Israel (S.R.C., T.T., M.O.F.);
University of Maryland Medical Intelligent Imaging (UM2ii) Center and Department
of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of
Medicine, Baltimore, Md (V.S.P.); The Russell H. Morgan Department of Radiology
and Radiological Science, The Johns Hopkins School of Medicine, Sidney Kimmel
Comprehensive Cancer Center, The Johns Hopkins School of Medicine, Baltimore, Md
(V.S.P., M.A.J.); Department of Diagnostic and Interventional Imaging, UT Health
at Houston, Houston, Tex (M.A.J.); Department of Radiological Sciences, David
Geffen School of Medicine, University of California, Los Angeles, Calif (R.Y.,
K.S.); Department of Bioengineering, Henry Samueli School of Engineering,
University of California, Los Angeles, Calif (R.Y., K.S.); Livestrong Cancer
Institutes (J.C.D., T.E.Y.), Departments of Biomedical Engineering, Diagnostic
Medicine, and Oncology (T.E.Y.), and The Oden Institute for Computational
Engineering and Sciences, The University of Texas at Austin, Austin, Tex
(J.C.D., T.E.Y.); Department of Imaging Physics, The University of Texas MD
Anderson Cancer Center, Houston, Tex (T.E.Y.); and Department of Radiology,
University of Michigan, Ann Arbor, Mich (T.L.C.)
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Lycke KD, Kalpathy-Cramer J, Jeronimo J, de Sanjose S, Egemen D, Del Pino M, Marcus J, Schiffman M, Hammer A. Agreement on Lesion Presence and Location at Colposcopy. J Low Genit Tract Dis 2024; 28:37-42. [PMID: 37963327 DOI: 10.1097/lgt.0000000000000786] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2023]
Abstract
OBJECTIVES/PURPOSE The reproducibility and sensitivity of image-based colposcopy is low, but agreement on lesion presence and location remains to be explored. Here, we investigate the interobserver agreement on lesions on colposcopic images by evaluating and comparing marked lesions on digitized colposcopic images between colposcopists. METHODS Five colposcopists reviewed images from 268 colposcopic examinations. Cases were selected based on histologic diagnosis, i.e., normal/cervical intraepithelial neoplasia (CIN)1 ( n = 50), CIN2 ( n = 50), CIN3 ( n = 100), adenocarcinoma in situ ( n = 53), and cancer ( n = 15). We obtained digitized time-series images every 7-10 seconds from before acetic acid application to 2 minutes after application. Colposcopists were instructed to digitally annotate all areas with acetowhitening or suspect of lesions. To estimate the agreement on lesion presence and location, we assessed the proportion of images with annotations and the proportion of images with overlapping annotated area by at least 4 (4+) colposcopists, respectively. RESULTS We included images from 241 examinations (1 image from each) with adequate annotations. The proportion with a least 1 lesion annotated by 4+ colposcopists increased by severity of histologic diagnosis. Among the CIN3 cases, 84% had at least 1 lesion annotated by 4+ colposcopists, whereas 54% of normal/CIN1 cases had a lesion annotated. Notably, the proportion was 70% for adenocarcinoma in situ and 71% for cancer. Regarding lesion location, there was no linear association with severity of histologic diagnosis. CONCLUSION Despite that 80% of the CIN2 and CIN3 cases were annotated by 4+ colposcopists, we did not find increasing agreement on lesion location with histology severity. This underlines the subjective nature of colposcopy.
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Affiliation(s)
| | | | | | | | | | | | - Jenna Marcus
- Feinberg School of Medicine at Northwestern University, Chicago, IL
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12
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Hoebel KV, Bridge CP, Ahmed S, Akintola O, Chung C, Huang RY, Johnson JM, Kim A, Ly KI, Chang K, Patel J, Pinho M, Batchelor TT, Rosen BR, Gerstner ER, Kalpathy-Cramer J. Expert-centered Evaluation of Deep Learning Algorithms for Brain Tumor Segmentation. Radiol Artif Intell 2024; 6:e220231. [PMID: 38197800 PMCID: PMC10831514 DOI: 10.1148/ryai.220231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Revised: 09/13/2023] [Accepted: 11/01/2023] [Indexed: 01/11/2024]
Abstract
Purpose To present results from a literature survey on practices in deep learning segmentation algorithm evaluation and perform a study on expert quality perception of brain tumor segmentation. Materials and Methods A total of 180 articles reporting on brain tumor segmentation algorithms were surveyed for the reported quality evaluation. Additionally, ratings of segmentation quality on a four-point scale were collected from medical professionals for 60 brain tumor segmentation cases. Results Of the surveyed articles, Dice score, sensitivity, and Hausdorff distance were the most popular metrics to report segmentation performance. Notably, only 2.8% of the articles included clinical experts' evaluation of segmentation quality. The experimental results revealed a low interrater agreement (Krippendorff α, 0.34) in experts' segmentation quality perception. Furthermore, the correlations between the ratings and commonly used quantitative quality metrics were low (Kendall tau between Dice score and mean rating, 0.23; Kendall tau between Hausdorff distance and mean rating, 0.51), with large variability among the experts. Conclusion The results demonstrate that quality ratings are prone to variability due to the ambiguity of tumor boundaries and individual perceptual differences, and existing metrics do not capture the clinical perception of segmentation quality. Keywords: Brain Tumor Segmentation, Deep Learning Algorithms, Glioblastoma, Cancer, Machine Learning Clinical trial registration nos. NCT00756106 and NCT00662506 Supplemental material is available for this article. © RSNA, 2023.
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Affiliation(s)
- Katharina V. Hoebel
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Christopher P. Bridge
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Sara Ahmed
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Oluwatosin Akintola
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Caroline Chung
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Raymond Y. Huang
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jason M. Johnson
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Albert Kim
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - K. Ina Ly
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Ken Chang
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jay Patel
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Marco Pinho
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Tracy T. Batchelor
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Bruce R. Rosen
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Elizabeth R. Gerstner
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
| | - Jayashree Kalpathy-Cramer
- From the Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology (K.V.H., C.P.B., A.K., K.I.L., K.C., J.P., B.R.R., E.R.G., J.K.C.), and Stephen E. and Catherine Pappas Center for Neuro-Oncology (O.A., A.K., K.I.L., E.R.G.), Massachusetts General Hospital, 149 13th St, Charlestown, MA 02129; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Mass (K.V.H., K.C., J.P.); MGH and BWH Center for Clinical Data Science, Boston, Mass (C.P.B., J.K.C.); Department of Radiation Oncology, Division of Radiation Oncology (S.A., C.C.); Department of Diagnostic Radiology, Division of Diagnostic Imaging (C.C.), and Department of Neuroradiology (J.M.J.), Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, Tex; Departments of Radiology (R.Y.H.) and Neurology (T.T.B.), Brigham and Women’s Hospital, Boston, Mass; Department of Radiology and Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, Tex (M.P.); and Department of Ophthalmology, University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.)
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Ahmed SR, Befano B, Lemay A, Egemen D, Rodriguez AC, Angara S, Desai K, Jeronimo J, Antani S, Campos N, Inturrisi F, Perkins R, Kreimer A, Wentzensen N, Herrero R, Del Pino M, Quint W, de Sanjose S, Schiffman M, Kalpathy-Cramer J. Reproducible and clinically translatable deep neural networks for cervical screening. Sci Rep 2023; 13:21772. [PMID: 38066031 PMCID: PMC10709439 DOI: 10.1038/s41598-023-48721-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 11/29/2023] [Indexed: 12/18/2023] Open
Abstract
Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. In this work, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-geography, multi-institution, and multi-device dataset of 9462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our model also produced reliable and consistent predictions, achieving a strong quadratic weighted kappa (QWK) of 0.86 and a minimal %2-class disagreement (% 2-Cl. D.) of 0.69%, between image pairs across women. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.
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Affiliation(s)
- Syed Rakin Ahmed
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02129, USA.
- Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, MA, 02115, USA.
- Massachusetts Institute of Technology, Cambridge, MA, 02139, USA.
- Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH, 03755, USA.
| | - Brian Befano
- Information Management Services, Calverton, MD, 20705, USA
- University of Washington, Seattle, WA, 98195, USA
| | - Andreanne Lemay
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02129, USA
- NeuroPoly, Polytechnique Montreal, Montreal, QC, H3T 1N8, Canada
| | - Didem Egemen
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Ana Cecilia Rodriguez
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Sandeep Angara
- Computational Health Research Branch, National Library of Medicine, Lister Hill Center, Bethesda, MD, 20894, USA
| | - Kanan Desai
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jose Jeronimo
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Sameer Antani
- Computational Health Research Branch, National Library of Medicine, Lister Hill Center, Bethesda, MD, 20894, USA
| | - Nicole Campos
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, MA, 02115, USA
| | - Federica Inturrisi
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Rebecca Perkins
- Department of Obstetrics & Gynecology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, 02118, USA
| | - Aimee Kreimer
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Nicolas Wentzensen
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Rolando Herrero
- Agencia Costarricense de Investigaciones Biomedicas (ACIB), Fundacion INCIENSA, San Jose, Costa Rica
| | | | - Wim Quint
- DDL Diagnostic Laboratory, Rijswijk, The Netherlands
| | - Silvia de Sanjose
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
- ISGlobal, Barcelona, Spain
| | - Mark Schiffman
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02129, USA
- Department of Ophthalmology, University of Colorado Anschutz, Denver, CO, 80045, USA
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Tan TF, Thirunavukarasu AJ, Campbell JP, Keane PA, Pasquale LR, Abramoff MD, Kalpathy-Cramer J, Lum F, Kim JE, Baxter SL, Ting DSW. Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges. Ophthalmol Sci 2023; 3:100394. [PMID: 37885755 PMCID: PMC10598525 DOI: 10.1016/j.xops.2023.100394] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 08/07/2023] [Accepted: 08/30/2023] [Indexed: 10/28/2023]
Abstract
The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: "large language models," "generative artificial intelligence," "ophthalmology," "ChatGPT," and "eye," based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders' perspectives-including patients, physicians, and policymakers-the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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Affiliation(s)
- Ting Fang Tan
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
| | - Arun James Thirunavukarasu
- University of Cambridge School of Clinical Medicine, Cambridge, United Kingdom
- Corpus Christi College, University of Cambridge, Cambridge, United Kingdom
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health and Science University, Portland, Oregon
| | - Pearse A. Keane
- Moorfields Eye Hospital, University of College London, London, United Kingdom
| | - Louis R. Pasquale
- Department of Ophthalmology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Michael D. Abramoff
- American Medical Association's Digital Medicine Payment Advisory Group (DMPAG) Artificial Intelligence Workgroup, American Medical Association, Chicago, Illinois
- Department of Ophthalmology, University of Iowa, Iowa City, Iowa
- Digital Diagnostics, Inc, Coralville, Iowa
| | | | - Flora Lum
- American Academy of Ophthalmology, San Francisco, California
| | - Judy E. Kim
- Department of Ophthalmology, Medical College of Wisconsin, Milwaukee, Wisconsin
| | - Sally L. Baxter
- Division of Ophthalmology Informatics and Data Science, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, La Jolla, California
- Health Department of Biomedical Informatics, University of California San Diego, La Jolla, California
| | - Daniel Shu Wei Ting
- Singapore Eye Research Institute, Singapore National Eye Centre, Singapore
- Byers Eye Institute, Stanford University, Stanford, California
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15
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Arsava EM, Chang K, Tawakol A, Loggia ML, Goldstein JN, Brown J, Park KY, Singhal AB, Kalpathy-Cramer J, Sorensen AG, Rosen BR, Samuels MA, Ay H. Stroke-Related Visceral Alterations: A Voxel-Based Neuroanatomic Localization Study. Ann Neurol 2023; 94:1155-1163. [PMID: 37642641 PMCID: PMC10841239 DOI: 10.1002/ana.26785] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 08/28/2023] [Accepted: 08/28/2023] [Indexed: 08/31/2023]
Abstract
OBJECTIVE Functional and morphologic changes in extracranial organs can occur after acute brain injury. The neuroanatomic correlates of such changes are not fully known. Herein, we tested the hypothesis that brain infarcts are associated with cardiac and systemic abnormalities (CSAs) in a regionally specific manner. METHODS We generated voxelwise p value maps of brain infarcts for poststroke plasma cardiac troponin T (cTnT) elevation, QTc prolongation, in-hospital infection, and acute stress hyperglycemia (ASH) in 1,208 acute ischemic stroke patients prospectively recruited into the Heart-Brain Interactions Study. We examined the relationship between infarct location and CSAs using a permutation-based approach and identified clusters of contiguous voxels associated with p < 0.05. RESULTS cTnT elevation not attributable to a known cardiac reason was detected in 5.5%, QTc prolongation in the absence of a known provoker in 21.2%, ASH in 33.9%, and poststroke infection in 13.6%. We identified significant, spatially segregated voxel clusters for each CSA. The clusters for troponin elevation and QTc prolongation mapped to the right hemisphere. There were 3 clusters for ASH, the largest of which was in the left hemisphere. We found 2 clusters for poststroke infection, one associated with pneumonia in the left and one with urinary tract infection in the right hemisphere. The relationship between infarct location and CSAs persisted after adjusting for infarct volume. INTERPRETATION Our results show that there are discrete regions of brain infarcts associated with CSAs. This information could be used to bootstrap toward new markers for better differentiation between neurogenic and non-neurogenic mechanisms of poststroke CSAs. ANN NEUROL 2023;94:1155-1163.
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Affiliation(s)
- Ethem Murat Arsava
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA, USA
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA
| | - Ahmed Tawakol
- Cardiology Division and Cardiovascular Imaging Research Center, Massachusetts General Hospital, Boston MA, USA
| | - Marco L. Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA, USA
| | - Joshua N. Goldstein
- Department of Emergency Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - James Brown
- School of Computer Science, University of Lincoln, Lincoln, United Kingdom
| | - Kwang-Yeol Park
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA, USA
- Department of Neurology, Chung-Ang University Hospital, Seoul, Republic of Korea
| | - Aneesh B. Singhal
- Department of Neurology, Massachusetts General Hospital, Boston MA, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA, USA
| | - Alma Gregory Sorensen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA, USA
| | - Bruce R. Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA, USA
| | | | - Hakan Ay
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown MA, USA
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Whitney HM, Baughan N, Myers KJ, Drukker K, Gichoya J, Bower B, Chen W, Gruszauskas N, Kalpathy-Cramer J, Koyejo S, Sá RC, Sahiner B, Zhang Z, Giger ML. Longitudinal assessment of demographic representativeness in the Medical Imaging and Data Resource Center open data commons. J Med Imaging (Bellingham) 2023; 10:61105. [PMID: 37469387 PMCID: PMC10353566 DOI: 10.1117/1.jmi.10.6.061105] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/21/2023] [Accepted: 06/23/2023] [Indexed: 07/21/2023] Open
Abstract
Purpose The Medical Imaging and Data Resource Center (MIDRC) open data commons was launched to accelerate the development of artificial intelligence (AI) algorithms to help address the COVID-19 pandemic. The purpose of this study was to quantify longitudinal representativeness of the demographic characteristics of the primary MIDRC dataset compared to the United States general population (US Census) and COVID-19 positive case counts from the Centers for Disease Control and Prevention (CDC). Approach The Jensen-Shannon distance (JSD), a measure of similarity of two distributions, was used to longitudinally measure the representativeness of the distribution of (1) all unique patients in the MIDRC data to the 2020 US Census and (2) all unique COVID-19 positive patients in the MIDRC data to the case counts reported by the CDC. The distributions were evaluated in the demographic categories of age at index, sex, race, ethnicity, and the combination of race and ethnicity. Results Representativeness of the MIDRC data by ethnicity and the combination of race and ethnicity was impacted by the percentage of CDC case counts for which this was not reported. The distributions by sex and race have retained their level of representativeness over time. Conclusion The representativeness of the open medical imaging datasets in the curated public data commons at MIDRC has evolved over time as the number of contributing institutions and overall number of subjects have grown. The use of metrics, such as the JSD support measurement of representativeness, is one step needed for fair and generalizable AI algorithm development.
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Affiliation(s)
- Heather M. Whitney
- University of Chicago, Chicago, Illinois, United States
- The Medical Imaging and Data Resource Center (midrc.org)
| | - Natalie Baughan
- University of Chicago, Chicago, Illinois, United States
- The Medical Imaging and Data Resource Center (midrc.org)
| | - Kyle J. Myers
- The Medical Imaging and Data Resource Center (midrc.org)
- Puente Solutions LLC, Phoenix, Arizona, United States
| | - Karen Drukker
- University of Chicago, Chicago, Illinois, United States
- The Medical Imaging and Data Resource Center (midrc.org)
| | - Judy Gichoya
- The Medical Imaging and Data Resource Center (midrc.org)
- Emory University, Atlanta, Georgia, United States
| | - Brad Bower
- The Medical Imaging and Data Resource Center (midrc.org)
- National Institutes of Health, Bethesda, Maryland, United States
| | - Weijie Chen
- The Medical Imaging and Data Resource Center (midrc.org)
- United States Food and Drug Administration, Silver Spring, Maryland, United States
| | - Nicholas Gruszauskas
- University of Chicago, Chicago, Illinois, United States
- The Medical Imaging and Data Resource Center (midrc.org)
| | - Jayashree Kalpathy-Cramer
- The Medical Imaging and Data Resource Center (midrc.org)
- University of Colorado Anschutz Medical Campus, Aurora, Colorado, United States
| | - Sanmi Koyejo
- The Medical Imaging and Data Resource Center (midrc.org)
- Stanford University, Stanford, California, United States
| | - Rui C. Sá
- The Medical Imaging and Data Resource Center (midrc.org)
- National Institutes of Health, Bethesda, Maryland, United States
- University of California, San Diego, La Jolla, California, United States
| | - Berkman Sahiner
- The Medical Imaging and Data Resource Center (midrc.org)
- United States Food and Drug Administration, Silver Spring, Maryland, United States
| | - Zi Zhang
- The Medical Imaging and Data Resource Center (midrc.org)
- Jefferson Health, Philadelphia, Pennsylvania, United States
| | - Maryellen L. Giger
- University of Chicago, Chicago, Illinois, United States
- The Medical Imaging and Data Resource Center (midrc.org)
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Drukker K, Chen W, Gichoya J, Gruszauskas N, Kalpathy-Cramer J, Koyejo S, Myers K, Sá RC, Sahiner B, Whitney H, Zhang Z, Giger M. Toward fairness in artificial intelligence for medical image analysis: identification and mitigation of potential biases in the roadmap from data collection to model deployment. J Med Imaging (Bellingham) 2023; 10:061104. [PMID: 37125409 PMCID: PMC10129875 DOI: 10.1117/1.jmi.10.6.061104] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 04/03/2023] [Indexed: 05/02/2023] Open
Abstract
Purpose To recognize and address various sources of bias essential for algorithmic fairness and trustworthiness and to contribute to a just and equitable deployment of AI in medical imaging, there is an increasing interest in developing medical imaging-based machine learning methods, also known as medical imaging artificial intelligence (AI), for the detection, diagnosis, prognosis, and risk assessment of disease with the goal of clinical implementation. These tools are intended to help improve traditional human decision-making in medical imaging. However, biases introduced in the steps toward clinical deployment may impede their intended function, potentially exacerbating inequities. Specifically, medical imaging AI can propagate or amplify biases introduced in the many steps from model inception to deployment, resulting in a systematic difference in the treatment of different groups. Approach Our multi-institutional team included medical physicists, medical imaging artificial intelligence/machine learning (AI/ML) researchers, experts in AI/ML bias, statisticians, physicians, and scientists from regulatory bodies. We identified sources of bias in AI/ML, mitigation strategies for these biases, and developed recommendations for best practices in medical imaging AI/ML development. Results Five main steps along the roadmap of medical imaging AI/ML were identified: (1) data collection, (2) data preparation and annotation, (3) model development, (4) model evaluation, and (5) model deployment. Within these steps, or bias categories, we identified 29 sources of potential bias, many of which can impact multiple steps, as well as mitigation strategies. Conclusions Our findings provide a valuable resource to researchers, clinicians, and the public at large.
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Affiliation(s)
- Karen Drukker
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Weijie Chen
- US Food and Drug Administration, Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - Judy Gichoya
- Emory University, Department of Radiology, Atlanta, Georgia, United States
| | - Nicholas Gruszauskas
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | | | - Sanmi Koyejo
- Stanford University, Department of Computer Science, Stanford, California, United States
| | - Kyle Myers
- Puente Solutions LLC, Phoenix, Arizona, United States
| | - Rui C. Sá
- National Institutes of Health, Bethesda, Maryland, United States
- University of California, San Diego, La Jolla, California, United States
| | - Berkman Sahiner
- US Food and Drug Administration, Division of Imaging, Diagnostics, and Software Reliability, Office of Science and Engineering Laboratories, Center for Devices and Radiological Health, Silver Spring, Maryland, United States
| | - Heather Whitney
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
| | - Zi Zhang
- Jefferson Health, Philadelphia, Pennsylvania, United States
| | - Maryellen Giger
- The University of Chicago, Department of Radiology, Chicago, Illinois, United States
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de Sanjosé S, Perkins RB, Campos NG, Inturrisi F, Egemen D, Befano B, Rodriguez AC, Jerónimo J, Cheung LC, Desai K, Han P, Novetsky AP, Ukwuani A, Marcus J, Ahmed SR, Wentzensen N, Kalpathy-Cramer J, Schiffman M. Design of the HPV-Automated Visual Evaluation (PAVE) Study: Validating a Novel Cervical Screening Strategy. medRxiv 2023:2023.08.30.23294826. [PMID: 37693492 PMCID: PMC10491363 DOI: 10.1101/2023.08.30.23294826] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Objective To describe the HPV-Automated Visual Evaluation (PAVE) Study, an international, multi-centric study designed to evaluate a novel cervical screen-triage-treat strategy for resource-limited settings as part of a global strategy to reduce cervical cancer burden. The PAVE strategy involves: 1) screening with self-sampled HPV testing; 2) triage of HPV-positive participants with a combination of extended genotyping and visual evaluation of the cervix assisted by deep-learning-based automated visual evaluation (AVE); and 3) treatment with thermal ablation or excision (Large Loop Excision of the Transformation Zone). The PAVE study has two phases: efficacy (2023-2024) and effectiveness (planned to begin in 2024-2025). The efficacy phase aims to refine and validate the screen-triage portion of the protocol. The effectiveness phase will examine acceptability and feasibility of the PAVE strategy into clinical practice, cost-effectiveness, and health communication within the PAVE sites. Study design Phase 1 Efficacy: Around 100,000 nonpregnant women, aged 25-49 years, without prior hysterectomy, and irrespective of HIV status, are being screened at nine study sites in resource-limited settings. Eligible and consenting participants perform self-collection of vaginal specimens for HPV testing using a FLOQSwab (Copan). Swabs are transported dry and undergo testing for HPV using a newly-redesigned isothermal DNA amplification HPV test (ScreenFire HPV RS), which has been designed to provide HPV genotyping by hierarchical risk groups: HPV16, else HPV18/45, else HPV31/33/35/52/58, else HPV39/51/56/59/68. HPV-negative individuals are considered negative for precancer/cancer and do not undergo further testing. HPV-positive individuals undergo pelvic examination with collection of cervical images and targeted biopsies of all acetowhite areas or endocervical sampling in the absence of visible lesions. Accuracy of histology diagnosis is evaluated across all sites. Cervical images are used to refine a deep learning AVE algorithm that classifies images as normal, indeterminate, or precancer+. AVE classifications are validated against the histologic endpoint of high-grade precancer determined by biopsy. The combination of HPV genotype and AVE classification is used to generate a risk score that corresponds to the risk of precancer (lower, medium, high, highest). During the efficacy phase, clinicians and patients within the PAVE sites will receive HPV testing results but not AVE results or risk scores. Treatment during the efficacy phase will be performed per local standard of care: positive Visual Inspection with Acetic Acid impression, high-grade colposcopic impression or CIN2+ on colposcopic biopsy, HPV positivity, or HPV 16,18/45 positivity. Follow up of triage negative patients and post treatment will follow standard of care protocols. The sensitivity of the PAVE strategy for detection of precancer will be compared to current SOC at a given level of specificity.Phase 2 Effectiveness: The AVE software will be downloaded to the new dedicated image analysis and thermal ablation devices (Liger Iris) into which the HPV genotype information can be entered to provide risk HPV-AVE risk scores for precancer to clinicians in real time. The effectiveness phase will examine clinician use of the PAVE strategy in practice, including feasibility and acceptability for clinicians and patients, cost-effectiveness, and health communication within the PAVE sites. Conclusion The goal of the PAVE study is to validate a screen-triage-treat protocol using novel biomarkers to provide an accurate, feasible, cost-effective strategy for cervical cancer prevention in resource-limited settings. If validated, implementation of PAVE at larger scale can be encouraged. Funding The consortial sites are responsible for their own study costs. Research equipment and supplies, and the NCI-affiliated staff are funded by the National Cancer Institute Intramural Research Program including supplemental funding from the Cancer Cures Moonshot Initiative. No commercial support was obtained. Brian Befano was supported by NCI/NIH under Grant T32CA09168. Date of protocol latest review: September 24 th 2023.
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Yang E, Li MD, Raghavan S, Deng F, Lang M, Succi MD, Huang AJ, Kalpathy-Cramer J. Transformer versus traditional natural language processing: how much data is enough for automated radiology report classification? Br J Radiol 2023; 96:20220769. [PMID: 37162253 PMCID: PMC10461267 DOI: 10.1259/bjr.20220769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2022] [Revised: 04/21/2023] [Accepted: 04/26/2023] [Indexed: 05/11/2023] Open
Abstract
OBJECTIVES Current state-of-the-art natural language processing (NLP) techniques use transformer deep-learning architectures, which depend on large training datasets. We hypothesized that traditional NLP techniques may outperform transformers for smaller radiology report datasets. METHODS We compared the performance of BioBERT, a deep-learning-based transformer model pre-trained on biomedical text, and three traditional machine-learning models (gradient boosted tree, random forest, and logistic regression) on seven classification tasks given free-text radiology reports. Tasks included detection of appendicitis, diverticulitis, bowel obstruction, and enteritis/colitis on abdomen/pelvis CT reports, ischemic infarct on brain CT/MRI reports, and medial and lateral meniscus tears on knee MRI reports (7,204 total annotated reports). The performance of NLP models on held-out test sets was compared after training using the full training set, and 2.5%, 10%, 25%, 50%, and 75% random subsets of the training data. RESULTS In all tested classification tasks, BioBERT performed poorly at smaller training sample sizes compared to non-deep-learning NLP models. Specifically, BioBERT required training on approximately 1,000 reports to perform similarly or better than non-deep-learning models. At around 1,250 to 1,500 training samples, the testing performance for all models began to plateau, where additional training data yielded minimal performance gain. CONCLUSIONS With larger sample sizes, transformer NLP models achieved superior performance in radiology report binary classification tasks. However, with smaller sizes (<1000) and more imbalanced training data, traditional NLP techniques performed better. ADVANCES IN KNOWLEDGE Our benchmarks can help guide clinical NLP researchers in selecting machine-learning models according to their dataset characteristics.
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Affiliation(s)
| | - Matthew D Li
- Department of Radiology and Diagnostic Imaging, University of Alberta, Edmonton, Alberta, Canada
| | - Shruti Raghavan
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Francis Deng
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Min Lang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Marc D Succi
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ambrose J Huang
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Lin HM, Colak E, Richards T, Kitamura FC, Prevedello LM, Talbott J, Ball RL, Gumeler E, Yeom KW, Hamghalam M, Simpson AL, Strika J, Bulja D, Angkurawaranon S, Pérez-Lara A, Gómez-Alonso MI, Ortiz Jiménez J, Peoples JJ, Law M, Dogan H, Altinmakas E, Youssef A, Mahfouz Y, Kalpathy-Cramer J, Flanders AE. The RSNA Cervical Spine Fracture CT Dataset. Radiol Artif Intell 2023; 5:e230034. [PMID: 37795143 PMCID: PMC10546361 DOI: 10.1148/ryai.230034] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 07/17/2023] [Accepted: 08/10/2023] [Indexed: 10/06/2023]
Abstract
This dataset is composed of cervical spine CT images with annotations related to fractures; it is available at https://www.kaggle.com/competitions/rsna-2022-cervical-spine-fracture-detection/.
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Affiliation(s)
- Hui Ming Lin
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Errol Colak
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Tyler Richards
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Felipe C. Kitamura
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Luciano M. Prevedello
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Jason Talbott
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Robyn L. Ball
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Ekim Gumeler
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Kristen W. Yeom
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Mohammad Hamghalam
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Amber L. Simpson
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Jasna Strika
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Deniz Bulja
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Salita Angkurawaranon
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Almudena Pérez-Lara
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - María Isabel Gómez-Alonso
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Johanna Ortiz Jiménez
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Jacob J. Peoples
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Meng Law
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Hakan Dogan
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Emre Altinmakas
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Ayda Youssef
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Yasser Mahfouz
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Jayashree Kalpathy-Cramer
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
| | - Adam E. Flanders
- From the Department of Medical Imaging, St Michael’s Hospital,
Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (H.M.L., E.C.);
Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada
(E.C.); Department of Radiology and Imaging Sciences, University of Utah, Salt
Lake City, Utah (T.R.); Dasa, Universidade Federal de São Paulo
(Unifesp), São Paulo, Brazil (F.C.K.); Department of Radiology, The Ohio
State University, Columbus, Ohio (L.M.P.); Department of Radiology and
Biomedical Imaging, University of California, San Francisco, San Francisco,
Calif (J.T.); The Jackson Laboratory, Bar Harbor, Maine (R.L.B.); Department of
Radiology, Hacettepe University, Ankara, Turkey (E.G.); Standard School of
Medicine, Stanford University, Stanford, Calif (K.W.Y.); School of Computing
(M.H., A.L.S., J.J.P.), Department of Biomedical and Molecular Sciences
(A.L.S.), and Department of Diagnostic Radiology (J.O.J.), Queen’s
University, Kingston, Ontario, Canada; Department of Biomedical Engineering,
Qazvin Branch, Islamic Azad University, Qazin, Iran (M.H.); Department of
Radiology, Cantonal Hospital Zenica, Zenica, Bosnia and Herzegovina (J.S.);
Clinic of Radiology, Clinical Center University of Sarajevo, Sarajevo, Bosnia
and Herzegovina (D.B.); Department of Radiology, Chiang Mai University, Chiang
Mai, Thailand (S.A.); Department of Radiology, Hospital Regional Universitario
de Málaga, Málaga, Spain (A.P.L.); Department of Radiology,
Hospital Quirónsalud Málaga, Málaga, Spain (M.I.G.A.);
Department of Radiology and Nuclear Medicine, Alfred Health, Monash University,
Melbourne, Australia (M.L.); Department of Radiology, Koç University
School of Medicine, Istanbul, Turkey (H.D., E.A.); Department of Diagnostic,
Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai,
New York, NY (E.A.); Department of Radiology, National Cancer Institute, Cairo
University, Cairo, Egypt (A.Y.); Department of Radiology, Sultan Qaboos
University Hospital, Muscat, Oman (Y.M.); Department of Ophthalmology,
University of Colorado Anschutz Medical Campus, Aurora, Colo (J.K.C.);
Department of Radiology and Athinoula A. Martinos Center for Biomedical Imaging,
Massachusetts General Hospital, Charlestown, Mass (J.K.C.); and Department of
Radiology, Division of Neuroradiology, Thomas Jefferson University,
Philadelphia, Pa (A.E.F.)
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Kim AE, Lou KW, Giobbie-Hurder A, Chang K, Gidwani M, Hoebel K, Patel JB, Cleveland MC, Singh P, Bridge CP, Ahmed SR, Bearce BA, Liu W, Fuster-Garcia E, Lee EQ, Lin NU, Overmoyer B, Wen PY, Nayak L, Cohen JV, Dietrich J, Eichler A, Heist R, Krop I, Lawrence D, Ligibel J, Tolaney S, Mayer E, Winer E, Perrino CM, Summers EJ, Mahar M, Oh K, Shih HA, Cahill DP, Rosen BR, Yen YF, Kalpathy-Cramer J, Martinez-Lage M, Sullivan RJ, Brastianos PK, Emblem KE, Gerstner ER. Structural and functional vascular dysfunction within brain metastases is linked to pembrolizumab inefficacy. bioRxiv 2023:2023.08.25.554868. [PMID: 37693537 PMCID: PMC10491098 DOI: 10.1101/2023.08.25.554868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Structurally and functionally aberrant vasculature is a hallmark of tumor angiogenesis and treatment resistance. Given the synergistic link between aberrant tumor vasculature and immunosuppression, we analyzed perfusion MRI for 44 patients with brain metastases (BM) undergoing treatment with pembrolizumab. To date, vascular-immune communication, or the relationship between immune checkpoint inhibitor (ICI) efficacy and vascular architecture, has not been well-characterized in human imaging studies. We found that ICI-responsive BM possessed a structurally balanced vascular makeup, which was linked to improved vascular efficiency and an immune-stimulatory microenvironment. In contrast, ICI-resistant BM were characterized by a lack of immune cell infiltration and a highly aberrant vasculature dominated by large-caliber vessels. Peri-tumor region analysis revealed early functional changes predictive of ICI resistance before radiographic evidence on conventional MRI. This study was one of the largest functional imaging studies for BM and establishes a foundation for functional studies that illuminate the mechanisms linking patterns of vascular architecture with immunosuppression, as targeting these aspects of cancer biology may serve as the basis for future combination treatments.
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22
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Lorenzo G, Ahmed SR, Ii DAH, Vaughn B, Kalpathy-Cramer J, Solorio L, Yankeelov TE, Gomez H. Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data. ArXiv 2023:arXiv:2308.14925v1. [PMID: 37693182 PMCID: PMC10491321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.
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23
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Bhandari SM, Singh P, Arun N, Sekimitsu S, Raghu V, Rauscher FG, Elze T, Horn K, Kirsten T, Scholz M, Segrè AV, Wiggs JL, Kalpathy-Cramer J, Zebardast N. Automated detection of genetic relatedness from fundus photographs using Siamese Neural Networks. medRxiv 2023:2023.08.16.23294183. [PMID: 37662422 PMCID: PMC10473808 DOI: 10.1101/2023.08.16.23294183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Heritability of common eye diseases and ocular traits are relatively high. Here, we develop an automated algorithm to detect genetic relatedness from color fundus photographs (FPs). We estimated the degree of shared ancestry amongst individuals in the UK Biobank using KING software. A convolutional Siamese neural network-based algorithm was trained to output a measure of genetic relatedness using 7224 pairs (3612 related and 3612 unrelated) of FPs. The model achieved high performance for prediction of genetic relatedness; when computed Euclidean distances were used to determine probability of relatedness, the area under the receiver operating characteristic curve (AUROC) for identifying related FPs reached 0.926. We performed external validation of our model using FPs from the LIFE-Adult study and achieved an AUROC of 0.69. An occlusion map indicates that the optic nerve and its surrounding area may be the most predictive of genetic relatedness. We demonstrate that genetic relatedness can be captured from FP features. This approach may be used to uncover novel biomarkers for common ocular diseases.
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24
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Ly KI, Richardson LG, Liu M, Muzikansky A, Cardona J, Lou K, Beers AL, Chang K, Brown JM, Ma X, Reardon DA, Arrillaga-Romany IC, Forst DA, Jordan JT, Lee EQ, Dietrich J, Nayak L, Wen PY, Chukwueke U, Giobbie-Hurder A, Choi BD, Batchelor TT, Kalpathy-Cramer J, Curry WT, Gerstner ER. Bavituximab Decreases Immunosuppressive Myeloid-Derived Suppressor Cells in Newly Diagnosed Glioblastoma Patients. Clin Cancer Res 2023; 29:3017-3025. [PMID: 37327319 DOI: 10.1158/1078-0432.ccr-23-0203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 03/29/2023] [Accepted: 06/13/2023] [Indexed: 06/18/2023]
Abstract
PURPOSE We evaluated the efficacy of bavituximab-a mAb with anti-angiogenic and immunomodulatory properties-in newly diagnosed patients with glioblastoma (GBM) who also received radiotherapy and temozolomide. Perfusion MRI and myeloid-related gene transcription and inflammatory infiltrates in pre-and post-treatment tumor specimens were studied to evaluate on-target effects (NCT03139916). PATIENTS AND METHODS Thirty-three adults with IDH--wild-type GBM received 6 weeks of concurrent chemoradiotherapy, followed by 6 cycles of temozolomide (C1-C6). Bavituximab was given weekly, starting week 1 of chemoradiotherapy, for at least 18 weeks. The primary endpoint was proportion of patients alive at 12 months (OS-12). The null hypothesis would be rejected if OS-12 was ≥72%. Relative cerebral blood flow (rCBF) and vascular permeability (Ktrans) were calculated from perfusion MRIs. Peripheral blood mononuclear cells and tumor tissue were analyzed pre-treatment and at disease progression using RNA transcriptomics and multispectral immunofluorescence for myeloid-derived suppressor cells (MDSC) and macrophages. RESULTS The study met its primary endpoint with an OS-12 of 73% (95% confidence interval, 59%-90%). Decreased pre-C1 rCBF (HR, 4.63; P = 0.029) and increased pre-C1 Ktrans were associated with improved overall survival (HR, 0.09; P = 0.005). Pre-treatment overexpression of myeloid-related genes in tumor tissue was associated with longer survival. Post-treatment tumor specimens contained fewer immunosuppressive MDSCs (P = 0.01). CONCLUSIONS Bavituximab has activity in newly diagnosed GBM and resulted in on-target depletion of intratumoral immunosuppressive MDSCs. Elevated pre-treatment expression of myeloid-related transcripts in GBM may predict response to bavituximab.
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Affiliation(s)
- K Ina Ly
- Stephen E. and Catherine Pappas Center for Neuro-Oncology Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Leland G Richardson
- Department of Neurosurgery Massachusetts General Hospital, Boston, Massachusetts
| | - Mofei Liu
- Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Alona Muzikansky
- Department of Biostatistics Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Jonathan Cardona
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Kevin Lou
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Andrew L Beers
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
| | - James M Brown
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - Xiaoyue Ma
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - David A Reardon
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Isabel C Arrillaga-Romany
- Stephen E. and Catherine Pappas Center for Neuro-Oncology Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Deborah A Forst
- Stephen E. and Catherine Pappas Center for Neuro-Oncology Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Justin T Jordan
- Stephen E. and Catherine Pappas Center for Neuro-Oncology Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Eudocia Q Lee
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Jorg Dietrich
- Stephen E. and Catherine Pappas Center for Neuro-Oncology Massachusetts General Hospital Cancer Center, Boston, Massachusetts
| | - Lakshmi Nayak
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Patrick Y Wen
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Ugonma Chukwueke
- Center for Neuro-Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Anita Giobbie-Hurder
- Division of Biostatistics, Department of Data Sciences, Dana-Farber Cancer Institute, Boston, Massachusetts
| | - Bryan D Choi
- Department of Neurosurgery Massachusetts General Hospital, Boston, Massachusetts
| | - Tracy T Batchelor
- Department of Neurology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
| | - William T Curry
- Department of Neurosurgery Massachusetts General Hospital, Boston, Massachusetts
| | - Elizabeth R Gerstner
- Stephen E. and Catherine Pappas Center for Neuro-Oncology Massachusetts General Hospital Cancer Center, Boston, Massachusetts
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, Massachusetts
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Deng B, Gu H, Zhu H, Chang K, Hoebel KV, Patel JB, Kalpathy-Cramer J, Carp SA. FDU-Net: Deep Learning-Based Three-Dimensional Diffuse Optical Image Reconstruction. IEEE Trans Med Imaging 2023; 42:2439-2450. [PMID: 37028063 PMCID: PMC10446911 DOI: 10.1109/tmi.2023.3252576] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Near-infrared diffuse optical tomography (DOT) is a promising functional modality for breast cancer imaging; however, the clinical translation of DOT is hampered by technical limitations. Specifically, conventional finite element method (FEM)-based optical image reconstruction approaches are time-consuming and ineffective in recovering full lesion contrast. To address this, we developed a deep learning-based reconstruction model (FDU-Net) comprised of a Fully connected subnet, followed by a convolutional encoder-Decoder subnet, and a U-Net for fast, end-to-end 3D DOT image reconstruction. The FDU-Net was trained on digital phantoms that include randomly located singular spherical inclusions of various sizes and contrasts. Reconstruction performance was evaluated in 400 simulated cases with realistic noise profiles for the FDU-Net and conventional FEM approaches. Our results show that the overall quality of images reconstructed by FDU-Net is significantly improved compared to FEM-based methods and a previously proposed deep-learning network. Importantly, once trained, FDU-Net demonstrates substantially better capability to recover true inclusion contrast and location without using any inclusion information during reconstruction. The model was also generalizable to multi-focal and irregularly shaped inclusions unseen during training. Finally, FDU-Net, trained on simulated data, could successfully reconstruct a breast tumor from a real patient measurement. Overall, our deep learning-based approach demonstrates marked superiority over the conventional DOT image reconstruction methods while also offering over four orders of magnitude acceleration in computational time. Once adapted to the clinical breast imaging workflow, FDU-Net has the potential to provide real-time accurate lesion characterization by DOT to assist the clinical diagnosis and management of breast cancer.
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26
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deCampos-Stairiker MA, Coyner AS, Gupta A, Oh M, Shah PK, Subramanian P, Venkatapathy N, Singh P, Kalpathy-Cramer J, Chiang MF, Chan RVP, Campbell JP. Epidemiologic Evaluation of Retinopathy of Prematurity Severity in a Large Telemedicine Program in India Using Artificial Intelligence. Ophthalmology 2023; 130:837-843. [PMID: 37030453 PMCID: PMC10524227 DOI: 10.1016/j.ophtha.2023.03.026] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 03/08/2023] [Accepted: 03/29/2023] [Indexed: 04/08/2023] Open
Abstract
PURPOSE Epidemiological changes in retinopathy of prematurity (ROP) depend on neonatal care, neonatal mortality, and the ability to carefully titrate and monitor oxygen. We evaluate whether an artificial intelligence (AI) algorithm for assessing ROP severity in babies can be used to evaluate changes in disease epidemiology in babies from South India over a 5-year period. DESIGN Retrospective cohort study. PARTICIPANTS Babies (3093) screened for ROP at neonatal care units (NCUs) across the Aravind Eye Care System (AECS) in South India. METHODS Images and clinical data were collected as part of routine tele-ROP screening at the AECS in India over 2 time periods: August 2015 to October 2017 and March 2019 to December 2020. All babies in the original cohort were matched 1:3 by birthweight (BW) and gestational age (GA) with babies in the later cohort. We compared the proportion of eyes with moderate (type 2) or treatment-requiring (TR) ROP, and an AI-derived ROP vascular severity score (from retinal fundus images) at the initial tele-retinal screening exam for all babies in a district, VSS), in the 2 time periods. MAIN OUTCOME MEASURES Differences in the proportions of type 2 or worse and TR-ROP cases, and VSS between time periods. RESULTS Among BW and GA matched babies, the proportion [95% confidence interval {CI}] of babies with type 2 or worse and TR-ROP decreased from 60.9% [53.8%-67.7%] to 17.1% [14.0%-20.5%] (P < 0.001) and 16.8% [11.9%-22.7%] to 5.1% [3.4%-7.3%] (P < 0.001), over the 2 time periods. Similarly, the median [interquartile range] VSS in the population decreased from 2.9 [1.2] to 2.4 [1.8] (P < 0.001). CONCLUSIONS In South India, over a 5-year period, the proportion of babies developing moderate to severe ROP has dropped significantly for babies at similar demographic risk, strongly suggesting improvements in primary prevention of ROP. These results suggest that AI-based assessment of ROP severity may be a useful epidemiologic tool to evaluate temporal changes in ROP epidemiology. FINANCIAL DISCLOSURE(S) Proprietary or commercial disclosure may be found after the references.
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Affiliation(s)
| | - Aaron S Coyner
- Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Aditi Gupta
- Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Minn Oh
- Ophthalmology, Oregon Health & Science University, Portland, Oregon
| | - Parag K Shah
- Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | - Prema Subramanian
- Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | | | - Praveer Singh
- Ophthalmology, University of Colorado, Aurora, Colorado; Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts
| | - Jayashree Kalpathy-Cramer
- Ophthalmology, University of Colorado, Aurora, Colorado; Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts
| | - Michael F Chiang
- National Eye Institute, National Institute of Health, Bethesda, Maryland; National Library of Medicine, National Institute of Health, Bethesda, Maryland
| | - R V Paul Chan
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - J Peter Campbell
- Ophthalmology, Oregon Health & Science University, Portland, Oregon.
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27
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Young BK, Cole ED, Shah PK, Ostmo S, Subramaniam P, Venkatapathy N, Tsai ASH, Coyner AS, Gupta A, Singh P, Chiang MF, Kalpathy-Cramer J, Chan RVP, Campbell JP. Efficacy of Smartphone-Based Telescreening for Retinopathy of Prematurity With and Without Artificial Intelligence in India. JAMA Ophthalmol 2023; 141:582-588. [PMID: 37166816 PMCID: PMC10176185 DOI: 10.1001/jamaophthalmol.2023.1466] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2023] [Accepted: 03/20/2023] [Indexed: 05/12/2023]
Abstract
Importance Retinopathy of prematurity (ROP) telemedicine screening programs have been found to be effective, but they rely on widefield digital fundus imaging (WDFI) cameras, which are expensive, making them less accessible in low- to middle-income countries. Cheaper, smartphone-based fundus imaging (SBFI) systems have been described, but these have a narrower field of view (FOV) and have not been tested in a real-world, operational telemedicine setting. Objective To assess the efficacy of SBFI systems compared with WDFI when used by technicians for ROP screening with both artificial intelligence (AI) and human graders. Design, Setting, and Participants This prospective cross-sectional comparison study took place as a single-center ROP teleophthalmology program in India from January 2021 to April 2022. Premature infants who met normal ROP screening criteria and enrolled in the teleophthalmology screening program were included. Those who had already been treated for ROP were excluded. Exposures All participants had WDFI images and from 1 of 2 SBFI devices, the Make-In-India (MII) Retcam or Keeler Monocular Indirect Ophthalmoscope (MIO) devices. Two masked readers evaluated zone, stage, plus, and vascular severity scores (VSS, from 1-9) in all images. Smartphone images were then stratified by patient into training (70%), validation (10%), and test (20%) data sets and used to train a ResNet18 deep learning architecture for binary classification of normal vs preplus or plus disease, which was then used for patient-level predictions of referral warranted (RW)- and treatment requiring (TR)-ROP. Main Outcome and Measures Sensitivity and specificity of detection of RW-ROP, and TR-ROP by both human graders and an AI system and area under the receiver operating characteristic curve (AUC) of grader-assigned VSS. Sensitivity and specificity were compared between the 2 SBFI systems using Pearson χ2testing. Results A total of 156 infants (312 eyes; mean [SD] gestational age, 33.0 [3.0] weeks; 75 [48%] female) were included with paired examinations. Sensitivity and specificity were not found to be statistically different between the 2 SBFI systems. Human graders were effective with SBFI at detecting TR-ROP with a sensitivity of 100% and specificity of 83.49%. The AUCs with grader-assigned VSS only were 0.95 (95% CI, 0.91-0.99) and 0.96 (95% CI, 0.93-0.99) for RW-ROP and TR-ROP, respectively. For the AI system, the sensitivity of detecting TR-ROP sensitivity was 100% with specificity of 58.6%, and RW-ROP sensitivity was 80.0% with specificity of 59.3%. Conclusions and Relevance In this cross-sectional study, 2 different SBFI systems used by technicians in an ROP screening program were highly sensitive for TR-ROP. SBFI systems with AI may be a cost-effective method to improve the global capacity for ROP screening.
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Affiliation(s)
- Benjamin K. Young
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - Emily D. Cole
- Department of Ophthalmology, University of Michigan, Ann Arbor
| | - Parag K. Shah
- Department of Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - Prema Subramaniam
- Department of Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | - Narendran Venkatapathy
- Department of Pediatric Retina and Ocular Oncology, Aravind Eye Hospital, Coimbatore, India
| | - Andrew S. H. Tsai
- Department of Surgical Retina, Singapore National Eye Center, Singapore
| | - Aaron S. Coyner
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - Aditi Gupta
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - Praveer Singh
- Department of Ophthalmology, University of Colorado, Aurora
| | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - Jayashree Kalpathy-Cramer
- Department of Ophthalmology, University of Colorado, Aurora
- Mass General Brigham and Brigham and Women’s Hospital Center for Clinical Data Science, Boston, Massachusetts
| | - R. V. Paul Chan
- Department of Ophthalmology, Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
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28
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Jang I, Hoffmann M, Singh N, Balbastre Y, Chen L, Rockenbach MABC, Dalca A, Aganj I, Kalpathy-Cramer J, Fischl B, Frost R. Clinical evaluation of k-space correlation informed motion artifact detection in segmented multi-slice MRI. Proc Int Soc Magn Reson Med Sci Meet Exhib Int Soc Magn Reson Med Sci Meet Exhib 2023; 2023:3425. [PMID: 37565069 PMCID: PMC10414784] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 08/12/2023]
Abstract
Motion artifacts can negatively impact diagnosis, patient experience, and radiology workflow especially when a patient recall is required. Detecting motion artifacts while the patient is still in the scanner could potentially improve workflow and reduce costs by enabling immediate corrective action. We demonstrate in a clinical k-space dataset that using cross-correlation between adjacent phase-encoding lines can detect motion artifacts directly from raw k-space in multi-shot multi-slice scans. We train a split-attention residual network to examine the performance in predicting motion artifact severity. The network is trained on simulated data and tested on real clinical data.
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Affiliation(s)
- Ikbeom Jang
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Malte Hoffmann
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Nalini Singh
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Yael Balbastre
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Lina Chen
- Data Science Office, Mass General Brigham, Boston, MA, United States
| | | | - Adrian Dalca
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Iman Aganj
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
| | - Bruce Fischl
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Robert Frost
- Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
- Department of Radiology, Harvard Medical School, Boston, MA, United States
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Coyner AS, Singh P, Brown JM, Ostmo S, Chan RP, Chiang MF, Kalpathy-Cramer J, Campbell JP. Association of Biomarker-Based Artificial Intelligence With Risk of Racial Bias in Retinal Images. JAMA Ophthalmol 2023; 141:543-552. [PMID: 37140902 PMCID: PMC10160994 DOI: 10.1001/jamaophthalmol.2023.1310] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2022] [Accepted: 03/01/2023] [Indexed: 05/05/2023]
Abstract
Importance Although race is a social construct, it is associated with variations in skin and retinal pigmentation. Image-based medical artificial intelligence (AI) algorithms that use images of these organs have the potential to learn features associated with self-reported race (SRR), which increases the risk of racially biased performance in diagnostic tasks; understanding whether this information can be removed, without affecting the performance of AI algorithms, is critical in reducing the risk of racial bias in medical AI. Objective To evaluate whether converting color fundus photographs to retinal vessel maps (RVMs) of infants screened for retinopathy of prematurity (ROP) removes the risk for racial bias. Design, Setting, and Participants The retinal fundus images (RFIs) of neonates with parent-reported Black or White race were collected for this study. A u-net, a convolutional neural network (CNN) that provides precise segmentation for biomedical images, was used to segment the major arteries and veins in RFIs into grayscale RVMs, which were subsequently thresholded, binarized, and/or skeletonized. CNNs were trained with patients' SRR labels on color RFIs, raw RVMs, and thresholded, binarized, or skeletonized RVMs. Study data were analyzed from July 1 to September 28, 2021. Main Outcomes and Measures Area under the precision-recall curve (AUC-PR) and area under the receiver operating characteristic curve (AUROC) at both the image and eye level for classification of SRR. Results A total of 4095 RFIs were collected from 245 neonates with parent-reported Black (94 [38.4%]; mean [SD] age, 27.2 [2.3] weeks; 55 majority sex [58.5%]) or White (151 [61.6%]; mean [SD] age, 27.6 [2.3] weeks, 80 majority sex [53.0%]) race. CNNs inferred SRR from RFIs nearly perfectly (image-level AUC-PR, 0.999; 95% CI, 0.999-1.000; infant-level AUC-PR, 1.000; 95% CI, 0.999-1.000). Raw RVMs were nearly as informative as color RFIs (image-level AUC-PR, 0.938; 95% CI, 0.926-0.950; infant-level AUC-PR, 0.995; 95% CI, 0.992-0.998). Ultimately, CNNs were able to learn whether RFIs or RVMs were from Black or White infants regardless of whether images contained color, vessel segmentation brightness differences were nullified, or vessel segmentation widths were uniform. Conclusions and Relevance Results of this diagnostic study suggest that it can be very challenging to remove information relevant to SRR from fundus photographs. As a result, AI algorithms trained on fundus photographs have the potential for biased performance in practice, even if based on biomarkers rather than raw images. Regardless of the methodology used for training AI, evaluating performance in relevant subpopulations is critical.
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Affiliation(s)
- Aaron S. Coyner
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - Praveer Singh
- Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts
- MGH & BWH Center for Clinical Data Science, Boston, Massachusetts
| | - James M. Brown
- School of Computer Science, University of Lincoln, Lincoln, United Kingdom
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
| | - R.V. Paul Chan
- Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago
| | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Jayashree Kalpathy-Cramer
- Radiology, MGH/Harvard Medical School, Charlestown, Massachusetts
- MGH & BWH Center for Clinical Data Science, Boston, Massachusetts
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland
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AlRyalat SA, Singh P, Kalpathy-Cramer J, Kahook MY. Artificial Intelligence and Glaucoma: Going Back to Basics. Clin Ophthalmol 2023; 17:1525-1530. [PMID: 37284059 PMCID: PMC10239633 DOI: 10.2147/opth.s410905] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/24/2023] [Indexed: 06/08/2023] Open
Abstract
There has been a recent surge in the number of publications centered on the use of artificial intelligence (AI) to diagnose various systemic diseases. The Food and Drug Administration has approved several algorithms for use in clinical practice. In ophthalmology, most advances in AI relate to diabetic retinopathy, which is a disease process with agreed upon diagnostic and classification criteria. However, this is not the case for glaucoma, which is a relatively complex disease without agreed-upon diagnostic criteria. Moreover, currently available public datasets that focus on glaucoma have inconstant label quality, further complicating attempts at training AI algorithms efficiently. In this perspective paper, we discuss specific details related to developing AI models for glaucoma and suggest potential steps to overcome current limitations.
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Affiliation(s)
| | - Praveer Singh
- Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USA
| | - Jayashree Kalpathy-Cramer
- Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USA
| | - Malik Y Kahook
- Department of Ophthalmology, University of Colorado School of Medicine, Sue Anschutz-Rodgers Eye Center, Aurora, CO, USA
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Cox M, Panagides JC, Di Capua J, Dua A, Kalva S, Kalpathy-Cramer J, Daye D. An interpretable machine learning model for the prevention of contrast-induced nephropathy in patients undergoing lower extremity endovascular interventions for peripheral arterial disease. Clin Imaging 2023; 101:1-7. [PMID: 37247523 DOI: 10.1016/j.clinimag.2023.05.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2022] [Revised: 04/26/2023] [Accepted: 05/22/2023] [Indexed: 05/31/2023]
Abstract
BACKGROUND Contrast-induced nephropathy (CIN) is a postprocedural complication associated with increased morbidity and mortality. An important risk factor for development of CIN is renal impairment. Identification of patients at risk for acute renal failure will allow physicians to make appropriate decisions to minimize the incidence of CIN. We developed a machine learning model to stratify risk of acute renal failure that may assist in mitigating risk for CIN in patients with peripheral artery disease (PAD) undergoing endovascular interventions. METHODS We utilized the American College of Surgeons National Surgical Quality Improvement Program database to extract clinical and laboratory information associated with 14,444 patients who underwent lower extremity endovascular procedures between 2011 and 2018. Using 11,604 cases from 2011 to 2017 for training and 2840 cases from 2018 for testing, we developed a random forest model to predict risk of 30-day acute renal failure following infra-inguinal endovascular procedures. RESULTS Eight variables were identified as contributing optimally to model predictions, the most important being diabetes, preoperative BUN, and claudication. Using these variables, the model achieved an area under the receiver-operating characteristic (AU-ROC) curve of 0.81, accuracy of 0.83, sensitivity of 0.67, and specificity of 0.74. The model performed equally well on white and nonwhite patients (Delong p-value = 0.955) and patients age < 65 and patients age ≥ 65 (Delong p-value = 0.659). CONCLUSIONS We develop a model that fairly and accurately stratifies 30-day acute renal failure risk in patients undergoing lower extremity endovascular procedures for PAD. This model may assist in identifying patients who may benefit from strategies to prevent CIN.
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Affiliation(s)
- Meredith Cox
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - J C Panagides
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - John Di Capua
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Anahita Dua
- Department of Surgery, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Sanjeeva Kalva
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | | | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
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Perkins RB, Smith DL, Jeronimo J, Campos NG, Gage JC, Hansen N, Rodriguez AC, Cheung LC, Egemen D, Befano B, Novetsky AP, Martins S, Kalpathy-Cramer J, Inturrisi F, Ahmed SR, Marcus J, Wentzensen N, de Sanjose S, Schiffman M. Use of risk-based cervical screening programs in resource-limited settings. Cancer Epidemiol 2023; 84:102369. [PMID: 37105017 DOI: 10.1016/j.canep.2023.102369] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Revised: 03/29/2023] [Accepted: 04/16/2023] [Indexed: 04/29/2023]
Abstract
Cervical cancer screening and management in the U.S. has adopted a risk-based approach. However, the majority of cervical cancer cases and deaths occur in resource-limited settings, where screening and management are not widely available. We describe a conceptual model that optimizes cervical cancer screening and management in resource-limited settings by utilizing a risk-based approach. The principles of risk-based screening and management in resource limited settings include (1) ensure that the screening method effectively separates low-risk from high-risk patients; (2) directing resources to populations at the highest cancer risk; (3) screen using HPV testing via self-sampling; (4) utilize HPV genotyping to improve risk stratification and better determine who will benefit from treatment, and (5) automated visual evaluation with artificial intelligence may further improve risk stratification. Risk-based screening and management in resource limited settings can optimize prevention by focusing triage and treatment resources on the highest risk patients while minimizing interventions in lower risk patients.
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Affiliation(s)
- Rebecca B Perkins
- Boston University Chobanian and Avedisian School of Medicine/Boston Medical Center, Boston, MA, USA.
| | | | | | - Nicole G Campos
- Center for Health Decision Science, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | | | | | | | - Li C Cheung
- National Cancer Institute, Bethesda, MD, USA
| | | | - Brian Befano
- Information Management Services Inc, 3901 Calverton Blvd Suite 200, Calverton, MD, USA
| | - Akiva P Novetsky
- Westchester Medical Center/New York Medical College, Valhalla, NY, USA
| | | | | | | | - Syed Rakin Ahmed
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02129, USA; Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, MA 02115, USA; Massachusetts Institute of Technology, Cambridge, MA 02139, USA; Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH 02139,USA
| | - Jenna Marcus
- Feinberg School of Medicine at Northwestern University, Chicago, IL, USA
| | | | - Silvia de Sanjose
- National Cancer Institute, Bethesda, MD, USA; ISGlobal, Barcelona, Spain
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Lauer A, Speroni SL, Choi M, Da X, Duncan C, McCarthy S, Krishnan V, Lusk CA, Rohde D, Hansen MB, Kalpathy-Cramer J, Loes DJ, Caruso PA, Williams DA, Mouridsen K, Emblem KE, Eichler FS, Musolino PL. Hematopoietic stem-cell gene therapy is associated with restored white matter microvascular function in cerebral adrenoleukodystrophy. Nat Commun 2023; 14:1900. [PMID: 37019892 PMCID: PMC10076264 DOI: 10.1038/s41467-023-37262-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Accepted: 03/07/2023] [Indexed: 04/07/2023] Open
Abstract
Blood-brain barrier disruption marks the onset of cerebral adrenoleukodystrophy (CALD), a devastating cerebral demyelinating disease caused by loss of ABCD1 gene function. The underlying mechanism are not well understood, but evidence suggests that microvascular dysfunction is involved. We analyzed cerebral perfusion imaging in boys with CALD treated with autologous hematopoietic stem-cells transduced with the Lenti-D lentiviral vector that contains ABCD1 cDNA as part of a single group, open-label phase 2-3 safety and efficacy study (NCT01896102) and patients treated with allogeneic hematopoietic stem cell transplantation. We found widespread and sustained normalization of white matter permeability and microvascular flow. We demonstrate that ABCD1 functional bone marrow-derived cells can engraft in the cerebral vascular and perivascular space. Inverse correlation between gene dosage and lesion growth suggests that corrected cells contribute long-term to remodeling of brain microvascular function. Further studies are needed to explore the longevity of these effects.
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Affiliation(s)
- Arne Lauer
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Department of Neuroradiology, Heidelberg University, Heidelberg, Germany
| | - Samantha L Speroni
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Myoung Choi
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Xiao Da
- Functional Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, USA
| | - Christine Duncan
- Dana-Farber and Boston Children's Cancer and Blood Disorders Center and Harvard Medical School, Boston, MA, USA
| | - Siobhan McCarthy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Vijai Krishnan
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Cole A Lusk
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - David Rohde
- Center for Systems Biology, Massachusetts General Hospital, Boston, MA, USA
| | - Mikkel Bo Hansen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | | | - Daniel J Loes
- Suburban Radiologic Consultants, Ltd, Minneapolis, MN, USA
| | - Paul A Caruso
- Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - David A Williams
- Dana-Farber and Boston Children's Cancer and Blood Disorders Center and Harvard Medical School, Boston, MA, USA
| | - Kim Mouridsen
- Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kyrre E Emblem
- Department of Diagnostic Physics, Oslo University Hospital, Oslo, Norway
| | - Florian S Eichler
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Patricia L Musolino
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Athinoula A. Martinos Centre for Biomedical Imaging, Charlestown, MA, USA.
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Gidwani M, Chang K, Patel JB, Hoebel KV, Ahmed SR, Singh P, Fuller CD, Kalpathy-Cramer J. Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models. Radiology 2023; 307:e220715. [PMID: 36537895 PMCID: PMC10068883 DOI: 10.1148/radiol.220715] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 10/19/2022] [Accepted: 11/01/2022] [Indexed: 12/24/2022]
Abstract
Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. While some studies report superlative accuracy of radiomic machine learning (ML) models, the published methodology is often incomplete, and the results are rarely validated in external testing data sets. Purpose To characterize the type, prevalence, and statistical impact of methodologic errors present in radiomic ML studies. Materials and Methods Radiomic ML publications were reviewed for the presence of performance-inflating methodologic flaws. Common flaws were subsequently reproduced with randomly generated features interpolated from publicly available radiomic data sets to demonstrate the precarious nature of reported findings. Results In an assessment of radiomic ML publications, the authors uncovered two general categories of data analysis errors: inconsistent partitioning and unproductive feature associations. In simulations, the authors demonstrated that inconsistent partitioning augments radiomic ML accuracy by 1.4 times from unbiased performance and that correcting for flawed methodologic results in areas under the receiver operating characteristic curve approaching a value of 0.5 (random chance). With use of randomly generated features, the authors illustrated that unproductive associations between radiomic features and gene sets can imply false causality for biologic phenomenon. Conclusion Radiomic machine learning studies may contain methodologic flaws that undermine their validity. This study provides a review template to avoid such flaws. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Jacobs in this issue.
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Affiliation(s)
- Mishka Gidwani
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Ken Chang
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Jay Biren Patel
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Katharina Viktoria Hoebel
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Syed Rakin Ahmed
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Praveer Singh
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Clifton David Fuller
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
| | - Jayashree Kalpathy-Cramer
- From the Athinoula A. Martinos Center for Biomedical Imaging (M.G.,
K.C., J.B.P., K.V.H., S.R.A., P.S., J.K.C.) and Department of Radiology
(J.K.C.), Massachusetts General Brigham, 13th St, Building 149, Room 2301,
Charlestown, MA 02129; Case Western School of Medicine, Cleveland, Ohio (M.G.);
Harvard-MIT Division of Health Sciences and Technology, Cambridge, Mass (J.B.P.,
K.V.H.); Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard
University, Cambridge, Mass (S.R.A.); Geisel School of Medicine at Dartmouth,
Dartmouth College, Hanover, NH (S.R.A.); and Department of Radiation Oncology,
The University of Texas MD Anderson Cancer Center, Houston, Tex (C.D.F.)
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Gupta S, Kumar S, Chang K, Lu C, Singh P, Kalpathy-Cramer J. Collaborative Privacy-preserving Approaches for Distributed Deep Learning Using Multi-Institutional Data. Radiographics 2023; 43:e220107. [PMID: 36862082 PMCID: PMC10091220 DOI: 10.1148/rg.220107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Revised: 08/04/2022] [Accepted: 08/09/2022] [Indexed: 03/03/2023]
Abstract
Deep learning (DL) algorithms have shown remarkable potential in automating various tasks in medical imaging and radiologic reporting. However, models trained on low quantities of data or only using data from a single institution often are not generalizable to other institutions, which may have different patient demographics or data acquisition characteristics. Therefore, training DL algorithms using data from multiple institutions is crucial to improving the robustness and generalizability of clinically useful DL models. In the context of medical data, simply pooling data from each institution to a central location to train a model poses several issues such as increased risk to patient privacy, increased costs for data storage and transfer, and regulatory challenges. These challenges of centrally hosting data have motivated the development of distributed machine learning techniques and frameworks for collaborative learning that facilitate the training of DL models without the need to explicitly share private medical data. The authors describe several popular methods for collaborative training and review the main considerations for deploying these models. They also highlight publicly available software frameworks for federated learning and showcase several real-world examples of collaborative learning. The authors conclude by discussing some key challenges and future research directions for distributed DL. They aim to introduce clinicians to the benefits, limitations, and risks of using distributed DL for the development of medical artificial intelligence algorithms. ©RSNA, 2023 Quiz questions for this article are available in the supplemental material.
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Affiliation(s)
| | | | - Ken Chang
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
13th Street, Building 149, Room 2301, Charlestown, MA 02129 (S.G., S.K., K.C.,
C.L., P.S., J.K.C.); and Indian Institute of Technology Delhi, New Delhi, India
(S.G., S.K.)
| | - Charles Lu
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
13th Street, Building 149, Room 2301, Charlestown, MA 02129 (S.G., S.K., K.C.,
C.L., P.S., J.K.C.); and Indian Institute of Technology Delhi, New Delhi, India
(S.G., S.K.)
| | - Praveer Singh
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
13th Street, Building 149, Room 2301, Charlestown, MA 02129 (S.G., S.K., K.C.,
C.L., P.S., J.K.C.); and Indian Institute of Technology Delhi, New Delhi, India
(S.G., S.K.)
| | - Jayashree Kalpathy-Cramer
- From the Athinoula A. Martinos Center for Biomedical Imaging,
Department of Radiology, Massachusetts General Hospital, Harvard Medical School,
13th Street, Building 149, Room 2301, Charlestown, MA 02129 (S.G., S.K., K.C.,
C.L., P.S., J.K.C.); and Indian Institute of Technology Delhi, New Delhi, India
(S.G., S.K.)
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Ahmed SR, Befano B, Lemay A, Egemen D, Rodriguez AC, Angara S, Desai K, Jeronimo J, Antani S, Campos N, Inturrisi F, Perkins R, Kreimer A, Wentzensen N, Herrero R, Del Pino M, Quint W, de Sanjose S, Schiffman M, Kalpathy-Cramer J. REPRODUCIBLE AND CLINICALLY TRANSLATABLE DEEP NEURAL NETWORKS FOR CANCER SCREENING. Res Sq 2023:rs.3.rs-2526701. [PMID: 36909463 PMCID: PMC10002800 DOI: 10.21203/rs.3.rs-2526701/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/06/2023]
Abstract
Cervical cancer is a leading cause of cancer mortality, with approximately 90% of the 250,000 deaths per year occurring in low- and middle-income countries (LMIC). Secondary prevention with cervical screening involves detecting and treating precursor lesions; however, scaling screening efforts in LMIC has been hampered by infrastructure and cost constraints. Recent work has supported the development of an artificial intelligence (AI) pipeline on digital images of the cervix to achieve an accurate and reliable diagnosis of treatable precancerous lesions. In particular, WHO guidelines emphasize visual triage of women testing positive for human papillomavirus (HPV) as the primary screen, and AI could assist in this triage task. Published AI reports have exhibited overfitting, lack of portability, and unrealistic, near-perfect performance estimates. To surmount recognized issues, we implemented a comprehensive deep-learning model selection and optimization study on a large, collated, multi-institutional dataset of 9,462 women (17,013 images). We evaluated relative portability, repeatability, and classification performance. The top performing model, when combined with HPV type, achieved an area under the Receiver Operating Characteristics (ROC) curve (AUC) of 0.89 within our study population of interest, and a limited total extreme misclassification rate of 3.4%, on held-aside test sets. Our work is among the first efforts at designing a robust, repeatable, accurate and clinically translatable deep-learning model for cervical screening.
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Affiliation(s)
- Syed Rakin Ahmed
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02129, USA
- Harvard Graduate Program in Biophysics, Harvard Medical School, Harvard University, Cambridge, MA 02115, USA
- Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Geisel School of Medicine at Dartmouth, Dartmouth College, Hanover, NH 02139,USA
| | - Brian Befano
- Information Management Services, Calverton, MD 20705, USA
- University of Washington, Seattle, WA 98195, USA
| | - Andreanne Lemay
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02129, USA
- NeuroPoly, Polytechnique Montreal, Montreal, QC H3T 1N8, Canada
| | - Didem Egemen
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Ana Cecilia Rodriguez
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Sandeep Angara
- Computational Health Research Branch, National Library of Medicine, Lister Hill Center, Bethesda, MD 20894
| | - Kanan Desai
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Jose Jeronimo
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Sameer Antani
- Computational Health Research Branch, National Library of Medicine, Lister Hill Center, Bethesda, MD 20894
| | - Nicole Campos
- Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston MA 02115
| | - Federica Inturrisi
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Rebecca Perkins
- Dept of Obstetrics & Gynecology, Boston University Chobanian & Avedisian School of Medicine, Boston, MA 02118
| | - Aimee Kreimer
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Nicolas Wentzensen
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Rolando Herrero
- Agencia Costarricense de Investigaciones Biomedicas (ACIB), Fundacion INCIENSA, San Jose, Costa Rica
| | | | - Wim Quint
- DDL Diagnostic Laboratory, Rijswijk, The Netherlands
| | - Silvia de Sanjose
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
- ISGlobal, Barcelona, Spain
| | - Mark Schiffman
- Clinical Epidemiology Unit, Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA 02129, USA
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Robinson-Weiss C, Patel J, Bizzo BC, Glazer DI, Bridge CP, Andriole KP, Dabiri B, Chin JK, Dreyer K, Kalpathy-Cramer J, Mayo-Smith WW. Machine Learning for Adrenal Gland Segmentation and Classification of Normal and Adrenal Masses at CT. Radiology 2023; 306:e220101. [PMID: 36125375 DOI: 10.1148/radiol.220101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023]
Abstract
Background Adrenal masses are common, but radiology reporting and recommendations for management can be variable. Purpose To create a machine learning algorithm to segment adrenal glands on contrast-enhanced CT images and classify glands as normal or mass-containing and to assess algorithm performance. Materials and Methods This retrospective study included two groups of contrast-enhanced abdominal CT examinations (development data set and secondary test set). Adrenal glands in the development data set were manually segmented by radiologists. Images in both the development data set and the secondary test set were manually classified as normal or mass-containing. Deep learning segmentation and classification models were trained on the development data set and evaluated on both data sets. Segmentation performance was evaluated with use of the Dice similarity coefficient (DSC), and classification performance with use of sensitivity and specificity. Results The development data set contained 274 CT examinations (251 patients; median age, 61 years; 133 women), and the secondary test set contained 991 CT examinations (991 patients; median age, 62 years; 578 women). The median model DSC on the development test set was 0.80 (IQR, 0.78-0.89) for normal glands and 0.84 (IQR, 0.79-0.90) for adrenal masses. On the development reader set, the median interreader DSC was 0.89 (IQR, 0.78-0.93) for normal glands and 0.89 (IQR, 0.85-0.97) for adrenal masses. Interreader DSC for radiologist manual segmentation did not differ from automated machine segmentation (P = .35). On the development test set, the model had a classification sensitivity of 83% (95% CI: 55, 95) and specificity of 89% (95% CI: 75, 96). On the secondary test set, the model had a classification sensitivity of 69% (95% CI: 58, 79) and specificity of 91% (95% CI: 90, 92). Conclusion A two-stage machine learning pipeline was able to segment the adrenal glands and differentiate normal adrenal glands from those containing masses. © RSNA, 2022 Online supplemental material is available for this article.
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Affiliation(s)
- Cory Robinson-Weiss
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Jay Patel
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Bernardo C Bizzo
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Daniel I Glazer
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Christopher P Bridge
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Katherine P Andriole
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Borna Dabiri
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - John K Chin
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Keith Dreyer
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - Jayashree Kalpathy-Cramer
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
| | - William W Mayo-Smith
- From the Department of Radiology, Brigham and Women's Hospital (BWH), Harvard Medical School, 75 Francis St, Boston, MA 02115 (C.R.W., D.I.G., K.P.A., B.D., W.W.M-S.); Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, Mass (J.P., C.P.B., J. Kalpathy-Cramer); Health Sciences and Technology Department, Massachusetts Institute of Technology, Cambridge, Mass (J.P.); Department of Radiology, Massachusetts General Hospital (MGH), Harvard Medical School, Boston, Mass (B.C.B., K.D.); and MGH & BWH Center for Clinical Data Science, Boston, Mass (B.C.B., C.P.B., K.P.A., J. K. Chin, K.D., J. Kalpathy-Cramer)
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Konz N, Buda M, Gu H, Saha A, Yang J, Chłędowski J, Park J, Witowski J, Geras KJ, Shoshan Y, Gilboa-Solomon F, Khapun D, Ratner V, Barkan E, Ozery-Flato M, Martí R, Omigbodun A, Marasinou C, Nakhaei N, Hsu W, Sahu P, Hossain MB, Lee J, Santos C, Przelaskowski A, Kalpathy-Cramer J, Bearce B, Cha K, Farahani K, Petrick N, Hadjiiski L, Drukker K, Armato SG, Mazurowski MA. A Competition, Benchmark, Code, and Data for Using Artificial Intelligence to Detect Lesions in Digital Breast Tomosynthesis. JAMA Netw Open 2023; 6:e230524. [PMID: 36821110 PMCID: PMC9951043 DOI: 10.1001/jamanetworkopen.2023.0524] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 02/24/2023] Open
Abstract
IMPORTANCE An accurate and robust artificial intelligence (AI) algorithm for detecting cancer in digital breast tomosynthesis (DBT) could significantly improve detection accuracy and reduce health care costs worldwide. OBJECTIVES To make training and evaluation data for the development of AI algorithms for DBT analysis available, to develop well-defined benchmarks, and to create publicly available code for existing methods. DESIGN, SETTING, AND PARTICIPANTS This diagnostic study is based on a multi-institutional international grand challenge in which research teams developed algorithms to detect lesions in DBT. A data set of 22 032 reconstructed DBT volumes was made available to research teams. Phase 1, in which teams were provided 700 scans from the training set, 120 from the validation set, and 180 from the test set, took place from December 2020 to January 2021, and phase 2, in which teams were given the full data set, took place from May to July 2021. MAIN OUTCOMES AND MEASURES The overall performance was evaluated by mean sensitivity for biopsied lesions using only DBT volumes with biopsied lesions; ties were broken by including all DBT volumes. RESULTS A total of 8 teams participated in the challenge. The team with the highest mean sensitivity for biopsied lesions was the NYU B-Team, with 0.957 (95% CI, 0.924-0.984), and the second-place team, ZeDuS, had a mean sensitivity of 0.926 (95% CI, 0.881-0.964). When the results were aggregated, the mean sensitivity for all submitted algorithms was 0.879; for only those who participated in phase 2, it was 0.926. CONCLUSIONS AND RELEVANCE In this diagnostic study, an international competition produced algorithms with high sensitivity for using AI to detect lesions on DBT images. A standardized performance benchmark for the detection task using publicly available clinical imaging data was released, with detailed descriptions and analyses of submitted algorithms accompanied by a public release of their predictions and code for selected methods. These resources will serve as a foundation for future research on computer-assisted diagnosis methods for DBT, significantly lowering the barrier of entry for new researchers.
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Affiliation(s)
- Nicholas Konz
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Mateusz Buda
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Hanxue Gu
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
| | - Ashirbani Saha
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
- Department of Oncology, McMaster University, Hamilton, Ontario, Canada
| | | | - Jakub Chłędowski
- Jagiellonian University, Kraków, Poland
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Jungkyu Park
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Jan Witowski
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Krzysztof J. Geras
- Department of Radiology, NYU Grossman School of Medicine, New York, New York
| | - Yoel Shoshan
- Medical Image Analytics, IBM Research, Haifa, Israel
| | | | - Daniel Khapun
- Medical Image Analytics, IBM Research, Haifa, Israel
| | - Vadim Ratner
- Medical Image Analytics, IBM Research, Haifa, Israel
| | - Ella Barkan
- Medical Image Analytics, IBM Research, Haifa, Israel
| | | | - Robert Martí
- Institute of Computer Vision and Robotics, University of Girona, Girona, Spain
| | - Akinyinka Omigbodun
- Medical and Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
| | - Chrysostomos Marasinou
- Medical and Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
| | - Noor Nakhaei
- Medical and Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
| | - William Hsu
- Medical and Imaging Informatics Group, Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
- Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles
- Department of Bioengineering, University of California Los Angeles Samueli School of Engineering
| | - Pranjal Sahu
- Department of Computer Science, Stony Brook University, Stony Brook, New York
| | - Md Belayat Hossain
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Juhun Lee
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania
| | - Carlos Santos
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
| | - Artur Przelaskowski
- Faculty of Mathematics and Information Science, Warsaw University of Technology, Warsaw, Poland
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown
| | - Benjamin Bearce
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown
| | - Kenny Cha
- US Food and Drug Administration, Silver Spring, Maryland
| | - Keyvan Farahani
- Center for Biomedical Informatics and Information Technology, National Cancer Institute, Bethesda, Maryland
| | | | | | - Karen Drukker
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Samuel G. Armato
- Department of Radiology, University of Chicago, Chicago, Illinois
| | - Maciej A. Mazurowski
- Department of Electrical and Computer Engineering, Duke University, Durham, North Carolina
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
- Department of Computer Science, Duke University, Durham, North Carolina
- Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, North Carolina
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Eilts SK, Pfeil JM, Poschkamp B, Krohne TU, Eter N, Barth T, Guthoff R, Lagrèze W, Grundel M, Bründer MC, Busch M, Kalpathy-Cramer J, Chiang MF, Chan RVP, Coyner AS, Ostmo S, Campbell JP, Stahl A. Assessment of Retinopathy of Prematurity Regression and Reactivation Using an Artificial Intelligence-Based Vascular Severity Score. JAMA Netw Open 2023; 6:e2251512. [PMID: 36656578 PMCID: PMC9857423 DOI: 10.1001/jamanetworkopen.2022.51512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
IMPORTANCE One of the biggest challenges when using anti-vascular endothelial growth factor (VEGF) agents to treat retinopathy of prematurity (ROP) is the need to perform long-term follow-up examinations to identify eyes at risk of ROP reactivation requiring retreatment. OBJECTIVE To evaluate whether an artificial intelligence (AI)-based vascular severity score (VSS) can be used to analyze ROP regression and reactivation after anti-VEGF treatment and potentially identify eyes at risk of ROP reactivation requiring retreatment. DESIGN, SETTING, AND PARTICIPANTS This prognostic study was a secondary analysis of posterior pole fundus images collected during the multicenter, double-blind, investigator-initiated Comparing Alternative Ranibizumab Dosages for Safety and Efficacy in Retinopathy of Prematurity (CARE-ROP) randomized clinical trial, which compared 2 different doses of ranibizumab (0.12 mg vs 0.20 mg) for the treatment of ROP. The CARE-ROP trial screened and enrolled infants between September 5, 2014, and July 14, 2016. A total of 1046 wide-angle fundus images obtained from 19 infants at predefined study time points were analyzed. The analyses of VSS were performed between January 20, 2021, and November 18, 2022. INTERVENTIONS An AI-based algorithm assigned a VSS between 1 (normal) and 9 (most severe) to fundus images. MAIN OUTCOMES AND MEASURES Analysis of VSS in infants with ROP over time and VSS comparisons between the 2 treatment groups (0.12 mg vs 0.20 mg of ranibizumab) and between infants who did and did not receive retreatment for ROP reactivation. RESULTS Among 19 infants with ROP in the CARE-ROP randomized clinical trial, the median (range) postmenstrual age at first treatment was 36.4 (34.7-39.7) weeks; 10 infants (52.6%) were male, and 18 (94.7%) were White. The mean (SD) VSS was 6.7 (1.9) at baseline and significantly decreased to 2.7 (1.9) at week 1 (P < .001) and 2.9 (1.3) at week 4 (P < .001). The mean (SD) VSS of infants with ROP reactivation requiring retreatment was 6.5 (1.9) at the time of retreatment, which was significantly higher than the VSS at week 4 (P < .001). No significant difference was found in VSS between the 2 treatment groups, but the change in VSS between baseline and week 1 was higher for infants who later required retreatment (mean [SD], 7.8 [1.3] at baseline vs 1.7 [0.7] at week 1) vs infants who did not (mean [SD], 6.4 [1.9] at baseline vs 3.0 [2.0] at week 1). In eyes requiring retreatment, higher baseline VSS was correlated with earlier time of retreatment (Pearson r = -0.9997; P < .001). CONCLUSIONS AND RELEVANCE In this study, VSS decreased after ranibizumab treatment, consistent with clinical disease regression. In cases of ROP reactivation requiring retreatment, VSS increased again to values comparable with baseline values. In addition, a greater change in VSS during the first week after initial treatment was found to be associated with a higher risk of later ROP reactivation, and high baseline VSS was correlated with earlier retreatment. These findings may have implications for monitoring ROP regression and reactivation after anti-VEGF treatment.
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Affiliation(s)
- Sonja K. Eilts
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Johanna M. Pfeil
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Broder Poschkamp
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Tim U. Krohne
- Department of Ophthalmology, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Nicole Eter
- Department of Ophthalmology, University of Muenster Medical Center, Muenster, Germany
| | - Teresa Barth
- Department of Ophthalmology, University of Regensburg, Regensburg, Germany
| | - Rainer Guthoff
- Department of Ophthalmology, Faculty of Medicine, University of Düsseldorf, Düsseldorf, Germany
| | - Wolf Lagrèze
- Eye Center, Medical Center, Faculty of Medicine, University of Freiburg, Freiburg, Germany
| | - Milena Grundel
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | | | - Martin Busch
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
| | - Jayashree Kalpathy-Cramer
- Center for Clinical Data Science, Massachusetts General Hospital, Brigham and Women’s Hospital, Boston
| | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland
| | - R. V. Paul Chan
- Department of Ophthalmology, University of Illinois Chicago, Chicago
| | - Aaron S. Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland
| | | | - Andreas Stahl
- Department of Ophthalmology, University Medicine Greifswald, Greifswald, Germany
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Sobhy M, Cole E, Jabbehdari S, Valikodath NG, Al-Khaled T, Kalinoski L, Chervinko M, Cherwek DH, Chuluunkhuu C, Shah PK, K C S, Jonas KE, Scanzera A, Yap VL, Yeh S, Kalpathy-Cramer J, Chiang MF, Campbell JP, Chan RVP. Operationalization of Retinopathy of Prematurity Screening by the Application of the Essential Public Health Services Framework. Int Ophthalmol Clin 2023; 63:39-63. [PMID: 36598833 PMCID: PMC9839316 DOI: 10.1097/iio.0000000000000448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Retinopathy of prematurity (ROP) is one of the leading causes of preventable pediatric blindness worldwide. ROP screening programs have been previously implemented in multiple low- and middle-income countries. On a global scale, it is crucial that evidence-based, standardized screening criteria are utilized in the early detection and treatment of ROP. In this review article, we utilize the National Public Health Performance Standards (NPHPS) Ten Essential Public Health Services Model organized by the core functions of assessment, policy development, and assurance to evaluate the barriers and successes of existing ROP screening programs. This framework can be applied to countries facing the third epidemic of ROP and can be used to establish a generalized model for eye care and screening worldwide.
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Cole E, Valikodath NG, Al-Khaled T, Bajimaya S, KC S, Chuluunbat T, Munkhuu B, Jonas KE, Chuluunkhuu C, MacKeen LD, Yap V, Hallak J, Ostmo S, Wu WC, Coyner AS, Singh P, Kalpathy-Cramer J, Chiang MF, Campbell JP, Chan RVP. Evaluation of an Artificial Intelligence System for Retinopathy of Prematurity Screening in Nepal and Mongolia. Ophthalmol Sci 2022; 2:100165. [PMID: 36531583 PMCID: PMC9754980 DOI: 10.1016/j.xops.2022.100165] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/29/2022] [Revised: 04/19/2022] [Accepted: 04/19/2022] [Indexed: 05/09/2023]
Abstract
PURPOSE To evaluate the performance of a deep learning (DL) algorithm for retinopathy of prematurity (ROP) screening in Nepal and Mongolia. DESIGN Retrospective analysis of prospectively collected clinical data. PARTICIPANTS Clinical information and fundus images were obtained from infants in 2 ROP screening programs in Nepal and Mongolia. METHODS Fundus images were obtained using the Forus 3nethra neo (Forus Health) in Nepal and the RetCam Portable (Natus Medical, Inc.) in Mongolia. The overall severity of ROP was determined from the medical record using the International Classification of ROP (ICROP). The presence of plus disease was determined independently in each image using a reference standard diagnosis. The Imaging and Informatics for ROP (i-ROP) DL algorithm was trained on images from the RetCam to classify plus disease and to assign a vascular severity score (VSS) from 1 through 9. MAIN OUTCOME MEASURES Area under the receiver operating characteristic curve and area under the precision-recall curve for the presence of plus disease or type 1 ROP and association between VSS and ICROP disease category. RESULTS The prevalence of type 1 ROP was found to be higher in Mongolia (14.0%) than in Nepal (2.2%; P < 0.001) in these data sets. In Mongolia (RetCam images), the area under the receiver operating characteristic curve for examination-level plus disease detection was 0.968, and the area under the precision-recall curve was 0.823. In Nepal (Forus images), these values were 0.999 and 0.993, respectively. The ROP VSS was associated with ICROP classification in both datasets (P < 0.001). At the population level, the median VSS was found to be higher in Mongolia (2.7; interquartile range [IQR], 1.3-5.4]) as compared with Nepal (1.9; IQR, 1.2-3.4; P < 0.001). CONCLUSIONS These data provide preliminary evidence of the effectiveness of the i-ROP DL algorithm for ROP screening in neonatal populations in Nepal and Mongolia using multiple camera systems and are useful for consideration in future clinical implementation of artificial intelligence-based ROP screening in low- and middle-income countries.
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Key Words
- Artificial intelligence
- BW, birth weight
- DL, deep learning
- Deep learning
- GA, gestational age
- ICROP, International Classification of Retinopathy of Prematurity
- IQR, interquartile range
- LMIC, low- and middle-income country
- Mongolia
- Nepal
- ROP, retinopathy of prematurity
- RSD, reference standard diagnosis
- Retinopathy of prematurity
- TR, treatment-requiring
- VSS, vascular severity score
- i-ROP, Imaging and Informatics for Retinopathy of Prematurity
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Affiliation(s)
- Emily Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | - Nita G. Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | | | - Sagun KC
- Helen Keller International, Kathmandu, Nepal
| | | | - Bayalag Munkhuu
- National Center for Maternal and Child Health, Ulaanbaatar, Mongolia
| | - Karyn E. Jonas
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | | | - Leslie D. MacKeen
- The Hospital for Sick Children, Toronto, Canada
- Phoenix Technology Group, Pleasanton, California
| | - Vivien Yap
- Department of Pediatrics, Weill Cornell Medical College, New York, New York
| | - Joelle Hallak
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Wei-Chi Wu
- Chang Gung Memorial Hospital, Taoyuan, Taiwan, and Chang Gung University, College of Medicine, Taoyuan, Taiwan
| | - Aaron S. Coyner
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | | | | | - Michael F. Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - J. Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - R. V. Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois Chicago, Chicago, Illinois
- Correspondence: R. V. Paul Chan, MD, MSc, MBA, Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, 1905 West Taylor Street, Chicago, IL 60612.
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Cole ED, Park SH, Kim SJ, Kang KB, Valikodath NG, Al-Khaled T, Patel SN, Jonas KE, Ostmo S, Coyner A, Berrocal A, Drenser KA, Nagiel A, Horowitz JD, Lee TC, Kalpathy-Cramer J, Chiang MF, Campbell JP, Chan RVP. Variability in Plus Disease Diagnosis using Single and Serial Images. Ophthalmol Retina 2022; 6:1122-1129. [PMID: 35659941 DOI: 10.1016/j.oret.2022.05.024] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2022] [Revised: 05/21/2022] [Accepted: 05/23/2022] [Indexed: 01/06/2023]
Abstract
PURPOSE To assess changes in retinopathy of prematurity (ROP) diagnosis in single and serial retinal images. DESIGN Cohort study. PARTICIPANTS Cases of ROP recruited from the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) consortium evaluated by 7 graders. METHODS Seven ophthalmologists reviewed both single and 3 consecutive serial retinal images from 15 cases with ROP, and severity was assigned as plus, preplus, or none. Imaging data were acquired during routine ROP screening from 2011 to 2015, and a reference standard diagnosis was established for each image. A secondary analysis was performed using the i-ROP deep learning system to assign a vascular severity score (VSS) to each image, ranging from 1 to 9, with 9 being the most severe disease. This score has been previously demonstrated to correlate with the International Classification of ROP. Mean plus disease severity was calculated by averaging 14 labels per image in serial and single images to decrease noise. MAIN OUTCOME MEASURES Grading severity of ROP as defined by plus, preplus, or no ROP. RESULTS Assessment of serial retinal images changed the grading severity for > 50% of the graders, although there was wide variability. Cohen's kappa ranged from 0.29 to 1.0, which showed a wide range of agreement from slight to perfect by each grader. Changes in the grading of serial retinal images were noted more commonly in cases of preplus disease. The mean severity in cases with a diagnosis of plus disease and no disease did not change between single and serial images. The ROP VSS demonstrated good correlation with the range of expert classifications of plus disease and overall agreement with the mode class (P = 0.001). The VSS correlated with mean plus disease severity by expert diagnosis (correlation coefficient, 0.89). The more aggressive graders tended to be influenced by serial images to increase the severity of their grading. The VSS also demonstrated agreement with disease progression across serial images, which progressed to preplus and plus disease. CONCLUSIONS Clinicians demonstrated variability in ROP diagnosis when presented with both single and serial images. The use of deep learning as a quantitative assessment of plus disease has the potential to standardize ROP diagnosis and treatment.
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Affiliation(s)
- Emily D Cole
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Shin Hae Park
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois; Department of Ophthalmology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea
| | - Sang Jin Kim
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon; Department of Ophthalmology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea
| | - Kai B Kang
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Nita G Valikodath
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Tala Al-Khaled
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | | | - Karyn E Jonas
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois
| | - Susan Ostmo
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Aaron Coyner
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - Audina Berrocal
- Bascom Palmer Eye Institute, University of Miami, Miami, Florida
| | - Kimberly A Drenser
- Department of Ophthalmology, Beaumont Eye Institute, Royal Oak, Michigan
| | - Aaron Nagiel
- Stein Eye Institute, University of California Los Angeles, Los Angeles, California
| | - Jason D Horowitz
- Department of Ophthalmology, Columbia University, New York, New York
| | - Thomas C Lee
- Roski Eye Institute, Department of Ophthalmology, Keck School of Medicine of the University of Southern California, Los Angeles, California
| | | | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - J Peter Campbell
- Department of Ophthalmology, Casey Eye Institute, Oregon Health & Science University, Portland, Oregon
| | - R V Paul Chan
- Department of Ophthalmology and Visual Sciences, Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago, Illinois.
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Cox M, Panagides JC, Tabari A, Kalva S, Kalpathy-Cramer J, Daye D. Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease. PLoS One 2022; 17:e0277507. [PMID: 36409699 PMCID: PMC9678279 DOI: 10.1371/journal.pone.0277507] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 10/28/2022] [Indexed: 11/22/2022] Open
Abstract
Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71-0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67-0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.
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Affiliation(s)
- Meredith Cox
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - J. C. Panagides
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Azadeh Tabari
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Sanjeeva Kalva
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
| | - Dania Daye
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States of America
- * E-mail:
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Trivedi A, Robinson C, Blazes M, Ortiz A, Desbiens J, Gupta S, Dodhia R, Bhatraju PK, Liles WC, Kalpathy-Cramer J, Lee AY, Lavista Ferres JM. Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement. PLoS One 2022; 17:e0274098. [PMID: 36201483 PMCID: PMC9536609 DOI: 10.1371/journal.pone.0274098] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Accepted: 08/22/2022] [Indexed: 11/07/2022] Open
Abstract
In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.
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Affiliation(s)
- Anusua Trivedi
- AI for Good Research Lab, Microsoft, Redmond, WA, United States of America
- Flipkart US R&D, Seattle, WA, United States of America
| | - Caleb Robinson
- AI for Good Research Lab, Microsoft, Redmond, WA, United States of America
| | - Marian Blazes
- Department of Ophthalmology, University of Washington, Seattle, WA, United States of America
| | - Anthony Ortiz
- AI for Good Research Lab, Microsoft, Redmond, WA, United States of America
| | - Jocelyn Desbiens
- Intelligent Retinal Imaging Systems, Pensacola, FL, United States of America
| | - Sunil Gupta
- Intelligent Retinal Imaging Systems, Pensacola, FL, United States of America
| | - Rahul Dodhia
- AI for Good Research Lab, Microsoft, Redmond, WA, United States of America
| | - Pavan K. Bhatraju
- Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, WA, United States of America
- Division of Pulmonary, Critical Care and Sleep Medicine, University of Washington, Seattle, WA, United States of America
| | - W. Conrad Liles
- Department of Medicine and Sepsis Center of Research Excellence, University of Washington (SCORE-UW), Seattle, WA, United States of America
| | | | - Aaron Y. Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, United States of America
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Brink L, Coombs LP, Kattil Veettil D, Kuchipudi K, Marella S, Schmidt K, Nair SS, Tilkin M, Treml C, Chang K, Kalpathy-Cramer J. ACR’s Connect and AI-LAB technical framework. JAMIA Open 2022; 5:ooac094. [PMID: 36380846 PMCID: PMC9651971 DOI: 10.1093/jamiaopen/ooac094] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Revised: 10/11/2022] [Accepted: 10/31/2022] [Indexed: 11/13/2022] Open
Abstract
Objective To develop a free, vendor-neutral software suite, the American College of Radiology (ACR) Connect, which serves as a platform for democratizing artificial intelligence (AI) for all individuals and institutions. Materials and Methods Among its core capabilities, ACR Connect provides educational resources; tools for dataset annotation; model building and evaluation; and an interface for collaboration and federated learning across institutions without the need to move data off hospital premises. Results The AI-LAB application within ACR Connect allows users to investigate AI models using their own local data while maintaining data security. The software enables non-technical users to participate in the evaluation and training of AI models as part of a larger, collaborative network. Discussion Advancements in AI have transformed automated quantitative analysis for medical imaging. Despite the significant progress in research, AI is currently underutilized in current clinical workflows. The success of AI model development depends critically on the synergy between physicians who can drive clinical direction, data scientists who can design effective algorithms, and the availability of high-quality datasets. ACR Connect and AI-LAB provide a way to perform external validation as well as collaborative, distributed training. Conclusion In order to create a collaborative AI ecosystem across clinical and technical domains, the ACR developed a platform that enables non-technical users to participate in education and model development.
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Affiliation(s)
- Laura Brink
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Laura P Coombs
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Deepak Kattil Veettil
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Kashyap Kuchipudi
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Sailaja Marella
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Kendall Schmidt
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Sujith Surendran Nair
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Michael Tilkin
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Christopher Treml
- Department of Information Technology, American College of Radiology , Reston, Virginia, USA
| | - Ken Chang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Boston, Massachusetts, USA
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital , Boston, Massachusetts, USA
- Department of Ophthalmology, University of Colorado School of Medicine , Aurora, Colorado, USA
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Kumar S, Lakshminarayanan A, Chang K, Guretno F, Mien IH, Kalpathy-Cramer J, Krishnaswamy P, Singh P. Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling. Distrib Collab Fed Learn Afford AI Healthc Resour Div Glob Health (2022) 2022; 13573:119-129. [PMID: 36745141 PMCID: PMC9890952 DOI: 10.1007/978-3-031-18523-6_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Federated Learning (FL) wherein multiple institutions collaboratively train a machine learning model without sharing data is becoming popular. Participating institutions might not contribute equally - some contribute more data, some better quality data or some more diverse data. To fairly rank the contribution of different institutions, Shapley value (SV) has emerged as the method of choice. Exact SV computation is impossibly expensive, especially when there are hundreds of contributors. Existing SV computation techniques use approximations. However, in healthcare where the number of contributing institutions are likely not of a colossal scale, computing exact SVs is still exorbitantly expensive, but not impossible. For such settings, we propose an efficient SV computation technique called SaFE (Shapley Value for Federated Learning using Ensembling). We empirically show that SaFE computes values that are close to exact SVs, and that it performs better than current SV approximations. This is particularly relevant in medical imaging setting where widespread heterogeneity across institutions is rampant and fast accurate data valuation is required to determine the contribution of each participant in multi-institutional collaborative learning.
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Affiliation(s)
- Sourav Kumar
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | - Ken Chang
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | - Feri Guretno
- Institute for Infocomm Research, ASTAR, Singapore
| | - Ivan Ho Mien
- Institute for Infocomm Research, ASTAR, Singapore
| | - Jayashree Kalpathy-Cramer
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
| | | | - Praveer Singh
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, USA
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Zhang M, Qu L, Singh P, Kalpathy-Cramer J, Rubin DL. SplitAVG: A Heterogeneity-Aware Federated Deep Learning Method for Medical Imaging. IEEE J Biomed Health Inform 2022; 26:4635-4644. [PMID: 35749336 PMCID: PMC9749741 DOI: 10.1109/jbhi.2022.3185956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Federated learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data. However, the data from different institutions are usually heterogeneous across institutions, which may reduce the performance of models trained using federated learning. In this study, we propose a novel heterogeneity-aware federated learning method, SplitAVG, to overcome the performance drops from data heterogeneity in federated learning. Unlike previous federated methods that require complex heuristic training or hyper parameter tuning, our SplitAVG leverages the simple network split and feature map concatenation strategies to encourage the federated model training an unbiased estimator of the target data distribution. We compare SplitAVG with seven state-of-the-art federated learning methods, using centrally hosted training data as the baseline on a suite of both synthetic and real-world federated datasets. We find that the performance of models trained using all the comparison federated learning methods degraded significantly with the increasing degrees of data heterogeneity. In contrast, SplitAVG method achieves comparable results to the baseline method under all heterogeneous settings, that it achieves 96.2% of the accuracy and 110.4% of the mean absolute error obtained by the baseline in a diabetic retinopathy binary classification dataset and a bone age prediction dataset, respectively, on highly heterogeneous data partitions. We conclude that SplitAVG method can effectively overcome the performance drops from variability in data distributions across institutions. Experimental results also show that SplitAVG can be adapted to different base convolutional neural networks (CNNs) and generalized to various types of medical imaging tasks. The code is publicly available at https://github.com/zm17943/SplitAVG.
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Mehta R, Filos A, Baid U, Sako C, McKinley R, Rebsamen M, Dätwyler K, Meier R, Radojewski P, Murugesan GK, Nalawade S, Ganesh C, Wagner B, Yu FF, Fei B, Madhuranthakam AJ, Maldjian JA, Daza L, Gómez C, Arbeláez P, Dai C, Wang S, Reynaud H, Mo Y, Angelini E, Guo Y, Bai W, Banerjee S, Pei L, AK M, Rosas-González S, Zemmoura I, Tauber C, Vu MH, Nyholm T, Löfstedt T, Ballestar LM, Vilaplana V, McHugh H, Maso Talou G, Wang A, Patel J, Chang K, Hoebel K, Gidwani M, Arun N, Gupta S, Aggarwal M, Singh P, Gerstner ER, Kalpathy-Cramer J, Boutry N, Huard A, Vidyaratne L, Rahman MM, Iftekharuddin KM, Chazalon J, Puybareau E, Tochon G, Ma J, Cabezas M, Llado X, Oliver A, Valencia L, Valverde S, Amian M, Soltaninejad M, Myronenko A, Hatamizadeh A, Feng X, Dou Q, Tustison N, Meyer C, Shah NA, Talbar S, Weber MA, Mahajan A, Jakab A, Wiest R, Fathallah-Shaykh HM, Nazeri A, Milchenko1 M, Marcus D, Kotrotsou A, Colen R, Freymann J, Kirby J, Davatzikos C, Menze B, Bakas S, Gal Y, Arbel T. QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results. J Mach Learn Biomed Imaging 2022; 2022:https://www.melba-journal.org/papers/2022:026.html. [PMID: 36998700 PMCID: PMC10060060] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 04/01/2023]
Abstract
Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://github.com/RagMeh11/QU-BraTS.
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Affiliation(s)
- Raghav Mehta
- Centre for Intelligent Machines (CIM), McGill University, Montreal, QC, Canada
| | - Angelos Filos
- Oxford Applied and Theoretical Machine Learning (OATML) Group, University of Oxford, Oxford, England
| | - Ujjwal Baid
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Chiharu Sako
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Richard McKinley
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Michael Rebsamen
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
| | - Katrin Dätwyler
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
- Human Performance Lab, Schulthess Clinic, Zurich, Switzerland
| | | | - Piotr Radojewski
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
| | | | - Sahil Nalawade
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Chandan Ganesh
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ben Wagner
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Fang F. Yu
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Baowei Fei
- Department of Bioengineering, University of Texas at Dallas, Texas, USA
| | - Ananth J. Madhuranthakam
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Joseph A. Maldjian
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Advanced Imaging Research Center, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Laura Daza
- Universidad de los Andes, Bogotá, Colombia
| | | | | | - Chengliang Dai
- Data Science Institute, Imperial College London, London, UK
| | - Shuo Wang
- Data Science Institute, Imperial College London, London, UK
| | | | - Yuanhan Mo
- Data Science Institute, Imperial College London, London, UK
| | - Elsa Angelini
- NIHR Imperial BRC, ITMAT Data Science Group, Imperial College London, London, UK
| | - Yike Guo
- Data Science Institute, Imperial College London, London, UK
| | - Wenjia Bai
- Data Science Institute, Imperial College London, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Subhashis Banerjee
- Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India
- Department of CSE, University of Calcutta, Kolkata, India
- Division of Visual Information and Interaction (Vi2), Department of Information Technology, Uppsala University, Uppsala, Sweden
| | - Linmin Pei
- Department of Diagnostic Radiology, The University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Murat AK
- Department of Diagnostic Radiology, The University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | | | - Ilyess Zemmoura
- UMR U1253 iBrain, Université de Tours, Inserm, Tours, France
- Neurosurgery department, CHRU de Tours, Tours, France
| | - Clovis Tauber
- UMR U1253 iBrain, Université de Tours, Inserm, Tours, France
| | - Minh H. Vu
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Tommy Löfstedt
- Department of Computing Science, Umeå University, Umeå, Sweden
| | - Laura Mora Ballestar
- Signal Theory and Communications Department, Universitat Politècnica de Catalunya, BarcelonaTech, Barcelona, Spain
| | - Veronica Vilaplana
- Signal Theory and Communications Department, Universitat Politècnica de Catalunya, BarcelonaTech, Barcelona, Spain
| | - Hugh McHugh
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Radiology Department, Auckland City Hospital, Auckland, New Zealand
| | | | - Alan Wang
- Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Auckland Bioengineering Institute, University of Auckland, New Zealand
| | - Jay Patel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Katharina Hoebel
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
- Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Mishka Gidwani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Nishanth Arun
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Sharut Gupta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Mehak Aggarwal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Praveer Singh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Elizabeth R. Gerstner
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, USA
| | - Nicolas Boutry
- EPITA Research and Development Laboratory (LRDE), France
| | - Alexis Huard
- EPITA Research and Development Laboratory (LRDE), France
| | - Lasitha Vidyaratne
- Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
| | - Md Monibor Rahman
- Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
| | - Khan M. Iftekharuddin
- Vision Lab, Electrical and Computer Engineering, Old Dominion University, Norfolk, VA 23529, USA
| | - Joseph Chazalon
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Biĉetre, France
| | - Elodie Puybareau
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Biĉetre, France
| | - Guillaume Tochon
- EPITA Research and Development Laboratory (LRDE), Le Kremlin-Biĉetre, France
| | - Jun Ma
- School of Science, Nanjing University of Science and Technology
| | - Mariano Cabezas
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Xavier Llado
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Arnau Oliver
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Liliana Valencia
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Sergi Valverde
- Research Institute of Computer Vision and Robotics, University of Girona, Spain
| | - Mehdi Amian
- Department of Electrical and Computer Engineering, University of Tehran, Iran
| | | | | | | | - Xue Feng
- Biomedical Engineering, University of Virginia, Charlottesville, USA
| | - Quan Dou
- Biomedical Engineering, University of Virginia, Charlottesville, USA
| | - Nicholas Tustison
- Radiology and Medical Imaging, University of Virginia, Charlottesville, USA
| | - Craig Meyer
- Biomedical Engineering, University of Virginia, Charlottesville, USA
- Radiology and Medical Imaging, University of Virginia, Charlottesville, USA
| | - Nisarg A. Shah
- Department of Electrical Engineering, Indian Institute of Technology - Jodhpur, Jodhpur, India
| | - Sanjay Talbar
- SGGS Institute of Engineering and Technology, Nanded, India
| | - Marc-André Weber
- Institute of Diagnostic and Interventional Radiology, Pediatric Radiology and Neuroradiology, University Medical Center Rostock, Rostock, Germany
| | - Abhishek Mahajan
- Tata Memorial Centre, Homi Bhabha National Institute, Mumbai, India
| | - Andras Jakab
- Center for MR-Research, University Children’s Hospital Zurich, Zurich, Switzerland
| | - Roland Wiest
- Support Center for Advanced Neuroimaging (SCAN), University Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Inselspital, Bern University Hospital, Bern, Switzerland
- Institute for Surgical Technology and Biomechanics, University of Bern, Bern, Switzerland
| | | | - Arash Nazeri
- Department of Radiology, Washington University, St. Louis, MO, USA
| | - Mikhail Milchenko1
- Department of Radiology, Washington University, St. Louis, MO, USA
- Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA
| | - Daniel Marcus
- Department of Radiology, Washington University, St. Louis, MO, USA
- Neuroimaging Informatics and Analysis Center, Washington University, St. Louis, MO, USA
| | - Aikaterini Kotrotsou
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Rivka Colen
- Department of Diagnostic Radiology, University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - John Freymann
- Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Justin Kirby
- Leidos Biomedical Research, Inc, Frederick National Laboratory for Cancer Research, Frederick, MD, USA
- Cancer Imaging Program, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Bjoern Menze
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
- Department of Informatics, Technical University of Munich, Munich, Germany
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA, USA
- Department of Radiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Yarin Gal
- Oxford Applied and Theoretical Machine Learning (OATML) Group, University of Oxford, Oxford, England
| | - Tal Arbel
- Centre for Intelligent Machines (CIM), McGill University, Montreal, QC, Canada
- MILA - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
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Li MD, Arun NT, Aggarwal M, Gupta S, Singh P, Little BP, Mendoza DP, Corradi GC, Takahashi MS, Ferraciolli SF, Succi MD, Lang M, Bizzo BC, Dayan I, Kitamura FC, Kalpathy-Cramer J. Multi-population generalizability of a deep learning-based chest radiograph severity score for COVID-19. Medicine (Baltimore) 2022; 101:e29587. [PMID: 35866818 PMCID: PMC9302282 DOI: 10.1097/md.0000000000029587] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2020] [Revised: 04/21/2022] [Accepted: 04/28/2022] [Indexed: 01/04/2023] Open
Abstract
To tune and test the generalizability of a deep learning-based model for assessment of COVID-19 lung disease severity on chest radiographs (CXRs) from different patient populations. A published convolutional Siamese neural network-based model previously trained on hospitalized patients with COVID-19 was tuned using 250 outpatient CXRs. This model produces a quantitative measure of COVID-19 lung disease severity (pulmonary x-ray severity (PXS) score). The model was evaluated on CXRs from 4 test sets, including 3 from the United States (patients hospitalized at an academic medical center (N = 154), patients hospitalized at a community hospital (N = 113), and outpatients (N = 108)) and 1 from Brazil (patients at an academic medical center emergency department (N = 303)). Radiologists from both countries independently assigned reference standard CXR severity scores, which were correlated with the PXS scores as a measure of model performance (Pearson R). The Uniform Manifold Approximation and Projection (UMAP) technique was used to visualize the neural network results. Tuning the deep learning model with outpatient data showed high model performance in 2 United States hospitalized patient datasets (R = 0.88 and R = 0.90, compared to baseline R = 0.86). Model performance was similar, though slightly lower, when tested on the United States outpatient and Brazil emergency department datasets (R = 0.86 and R = 0.85, respectively). UMAP showed that the model learned disease severity information that generalized across test sets. A deep learning model that extracts a COVID-19 severity score on CXRs showed generalizable performance across multiple populations from 2 continents, including outpatients and hospitalized patients.
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Affiliation(s)
- Matthew D. Li
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Nishanth T. Arun
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Mehak Aggarwal
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sharut Gupta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Praveer Singh
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Brent P. Little
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Dexter P. Mendoza
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | | | | | - Marc D. Succi
- Division of Emergency Radiology, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Min Lang
- Division of Thoracic Imaging and Intervention, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bernardo C. Bizzo
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- MGH and BWH Center for Clinical Data Science, Mass General Brigham, Boston, MA, USA
| | - Ittai Dayan
- MGH and BWH Center for Clinical Data Science, Mass General Brigham, Boston, MA, USA
| | - Felipe C. Kitamura
- Diagnósticos da América SA (DASA), São Paulo, Brazil
- Department of Diagnostic Imaging, Universidade Federal de São Paulo, São Paulo, Brazil
| | - Jayashree Kalpathy-Cramer
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- MGH and BWH Center for Clinical Data Science, Mass General Brigham, Boston, MA, USA
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50
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Coyner AS, Oh MA, Shah PK, Singh P, Ostmo S, Valikodath NG, Cole E, Al-Khaled T, Bajimaya S, K C S, Chuluunbat T, Munkhuu B, Subramanian P, Venkatapathy N, Jonas KE, Hallak JA, Chan RVP, Chiang MF, Kalpathy-Cramer J, Campbell JP. External Validation of a Retinopathy of Prematurity Screening Model Using Artificial Intelligence in 3 Low- and Middle-Income Populations. JAMA Ophthalmol 2022; 140:791-798. [PMID: 35797036 PMCID: PMC9264225 DOI: 10.1001/jamaophthalmol.2022.2135] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
Importance Retinopathy of prematurity (ROP) is a leading cause of preventable blindness that disproportionately affects children born in low- and middle-income countries (LMICs). In-person and telemedical screening examinations can reduce this risk but are challenging to implement in LMICs owing to the multitude of at-risk infants and lack of trained ophthalmologists. Objective To implement an ROP risk model using retinal images from a single baseline examination to identify infants who will develop treatment-requiring (TR)-ROP in LMIC telemedicine programs. Design, Setting, and Participants In this diagnostic study conducted from February 1, 2019, to June 30, 2021, retinal fundus images were collected from infants as part of an Indian ROP telemedicine screening program. An artificial intelligence (AI)-derived vascular severity score (VSS) was obtained from images from the first examination after 30 weeks' postmenstrual age. Using 5-fold cross-validation, logistic regression models were trained on 2 variables (gestational age and VSS) for prediction of TR-ROP. The model was externally validated on test data sets from India, Nepal, and Mongolia. Data were analyzed from October 20, 2021, to April 20, 2022. Main Outcomes and Measures Primary outcome measures included sensitivity, specificity, positive predictive value, and negative predictive value for predictions of future occurrences of TR-ROP; the number of weeks before clinical diagnosis when a prediction was made; and the potential reduction in number of examinations required. Results A total of 3760 infants (median [IQR] postmenstrual age, 37 [5] weeks; 1950 male infants [51.9%]) were included in the study. The diagnostic model had a sensitivity and specificity, respectively, for each of the data sets as follows: India, 100.0% (95% CI, 87.2%-100.0%) and 63.3% (95% CI, 59.7%-66.8%); Nepal, 100.0% (95% CI, 54.1%-100.0%) and 77.8% (95% CI, 72.9%-82.2%); and Mongolia, 100.0% (95% CI, 93.3%-100.0%) and 45.8% (95% CI, 39.7%-52.1%). With the AI model, infants with TR-ROP were identified a median (IQR) of 2.0 (0-11) weeks before TR-ROP diagnosis in India, 0.5 (0-2.0) weeks before TR-ROP diagnosis in Nepal, and 0 (0-5.0) weeks before TR-ROP diagnosis in Mongolia. If low-risk infants were never screened again, the population could be effectively screened with 45.0% (India, 664/1476), 38.4% (Nepal, 151/393), and 51.3% (Mongolia, 266/519) fewer examinations required. Conclusions and Relevance Results of this diagnostic study suggest that there were 2 advantages to implementation of this risk model: (1) the number of examinations for low-risk infants could be reduced without missing cases of TR-ROP, and (2) high-risk infants could be identified and closely monitored before development of TR-ROP.
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Affiliation(s)
- Aaron S Coyner
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Minn A Oh
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Parag K Shah
- Pediatric Retina and Ocular Oncology Division, Aravind Eye Hospital, Coimbatore, India
| | - Praveer Singh
- Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science, Boston, Massachusetts.,Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, Massachusetts
| | - Susan Ostmo
- Casey Eye Institute, Oregon Health & Science University, Portland
| | - Nita G Valikodath
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
| | - Emily Cole
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
| | - Tala Al-Khaled
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
| | | | - Sagun K C
- Helen Keller International, Kathmandu, Nepal
| | | | - Bayalag Munkhuu
- National Center for Maternal and Child Health, Ulaanbaatar, Mongolia
| | - Prema Subramanian
- Pediatric Retina and Ocular Oncology Division, Aravind Eye Hospital, Coimbatore, India
| | | | - Karyn E Jonas
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
| | - Joelle A Hallak
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
| | - R V Paul Chan
- Illinois Eye and Ear Infirmary, University of Illinois at Chicago, Chicago
| | - Michael F Chiang
- National Eye Institute, National Institutes of Health, Bethesda, Maryland
| | - Jayashree Kalpathy-Cramer
- Massachusetts General Hospital and Brigham and Women's Hospital Center for Clinical Data Science, Boston, Massachusetts.,Radiology, Massachusetts General Hospital/Harvard Medical School, Charlestown, Massachusetts
| | - J Peter Campbell
- Casey Eye Institute, Oregon Health & Science University, Portland
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