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Cai TN, Zhao L, Yang Y, Mao HM, Huang SG, Guo WL. Development of a CT-based radiomics-clinical model to diagnose acute pancreatitis on nonobvious findings on CT in children with pancreaticobiliary maljunction. Br J Radiol 2024; 97:1029-1037. [PMID: 38460184 DOI: 10.1093/bjr/tqae054] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2023] [Revised: 02/19/2024] [Accepted: 03/06/2024] [Indexed: 03/11/2024] Open
Abstract
OBJECTIVES Since neither abdominal pain nor pancreatic enzyme elevation is specific for acute pancreatitis (AP), the diagnosis of AP in patients with pancreaticobiliary maljunction (PBM) may be challenging when the pancreas appears normal or nonobvious on CT. This study aimed to develop a quantitative radiomics-based nomogram of pancreatic CT for identifying AP in children with PBM who have nonobvious findings on CT. METHODS PBM patients with a diagnosis of AP evaluated at the Children's Hospital of Soochow University from June 2015 to October 2022 were retrospectively reviewed. The radiological features and clinical factors associated with AP were evaluated. Based on the selected variables, multivariate logistic regression was used to construct clinical, radiomics, and combined models. RESULTS Two clinical parameters and 6 radiomics characteristics were chosen based on their significant association with AP, as demonstrated in the training (area under curve [AUC]: 0.767, 0.892) and validation (AUC: 0.757, 0.836) datasets. The radiomics-clinical nomogram demonstrated superior performance in both the training (AUC, 0.938) and validation (AUC, 0.864) datasets, exhibiting satisfactory calibration (P > .05). CONCLUSIONS Our radiomics-based nomogram is an accurate, noninvasive diagnostic technique that can identify AP in children with PBM even when CT presentation is not obvious. ADVANCES IN KNOWLEDGE This study extracted imaging features of nonobvious pancreatitis. Then it developed and evaluated a combined model with these features.
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Affiliation(s)
- Tian-Na Cai
- Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China
| | - Lian Zhao
- Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China
| | - Yang Yang
- Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China
| | - Hui-Min Mao
- Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China
| | - Shun-Gen Huang
- Pediatric Surgery, Children's Hospital of Soochow University, Suzhou 215025, China
| | - Wan-Liang Guo
- Department of Radiology, Children's Hospital of Soochow University, Suzhou 215025, China
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Martinez Luque E, Liu Z, Sung D, Goldberg RM, Agarwal R, Bhattacharya A, Ahmed NS, Allen JW, Fleischer CC. An Update on MR Spectroscopy in Cancer Management: Advances in Instrumentation, Acquisition, and Analysis. Radiol Imaging Cancer 2024; 6:e230101. [PMID: 38578207 DOI: 10.1148/rycan.230101] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/06/2024]
Abstract
MR spectroscopy (MRS) is a noninvasive imaging method enabling chemical and molecular profiling of tissues in a localized, multiplexed, and nonionizing manner. As metabolic reprogramming is a hallmark of cancer, MRS provides valuable metabolic and molecular information for cancer diagnosis, prognosis, treatment monitoring, and patient management. This review provides an update on the use of MRS for clinical cancer management. The first section includes an overview of the principles of MRS, current methods, and conventional metabolites of interest. The remainder of the review is focused on three key areas: advances in instrumentation, specifically ultrahigh-field-strength MRI scanners and hybrid systems; emerging methods for acquisition, including deuterium imaging, hyperpolarized carbon 13 MRI and MRS, chemical exchange saturation transfer, diffusion-weighted MRS, MR fingerprinting, and fast acquisition; and analysis aided by artificial intelligence. The review concludes with future recommendations to facilitate routine use of MRS in cancer management. Keywords: MR Spectroscopy, Spectroscopic Imaging, Molecular Imaging in Oncology, Metabolic Reprogramming, Clinical Cancer Management © RSNA, 2024.
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Affiliation(s)
- Eva Martinez Luque
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
| | - Zexuan Liu
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
| | - Dongsuk Sung
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
| | - Rachel M Goldberg
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
| | - Rishab Agarwal
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
| | - Aditya Bhattacharya
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
| | - Nadine S Ahmed
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
| | - Jason W Allen
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
| | - Candace C Fleischer
- From the Departments of Radiology and Imaging Sciences (E.M.L., Z.L., D.S., J.W.A., C.C.F.) and Neurology (J.W.A.), Emory University School of Medicine, Atlanta, Ga; Department of Biomedical Engineering (E.M.L., Z.L., D.S., J.W.A., C.C.F.), Georgia Institute of Technology and Emory University, Atlanta, Ga; College of Arts and Sciences, Emory University, Atlanta, Ga (R.M.G.); and College of Business (R.A.) and College of Sciences (A.B., N.S.A.), Georgia Institute of Technology, Atlanta, Georgia
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García-Figueiras R, Baleato-González S, Luna A, Padhani AR, Vilanova JC, Carballo-Castro AM, Oleaga-Zufiria L, Vallejo-Casas JA, Marhuenda A, Gómez-Caamaño A. How Imaging Advances Are Defining the Future of Precision Radiation Therapy. Radiographics 2024; 44:e230152. [PMID: 38206833 DOI: 10.1148/rg.230152] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/13/2024]
Abstract
Radiation therapy is fundamental in the treatment of cancer. Imaging has always played a central role in radiation oncology. Integrating imaging technology into irradiation devices has increased the precision and accuracy of dose delivery and decreased the toxic effects of the treatment. Although CT has become the standard imaging modality in radiation therapy, the development of recently introduced next-generation imaging techniques has improved diagnostic and therapeutic decision making in radiation oncology. Functional and molecular imaging techniques, as well as other advanced imaging modalities such as SPECT, yield information about the anatomic and biologic characteristics of tumors for the radiation therapy workflow. In clinical practice, they can be useful for characterizing tumor phenotypes, delineating volumes, planning treatment, determining patients' prognoses, predicting toxic effects, assessing responses to therapy, and detecting tumor relapse. Next-generation imaging can enable personalization of radiation therapy based on a greater understanding of tumor biologic factors. It can be used to map tumor characteristics, such as metabolic pathways, vascularity, cellular proliferation, and hypoxia, that are known to define tumor phenotype. It can also be used to consider tumor heterogeneity by highlighting areas at risk for radiation resistance for focused biologic dose escalation, which can impact the radiation planning process and patient outcomes. The authors review the possible contributions of next-generation imaging to the treatment of patients undergoing radiation therapy. In addition, the possible roles of radio(geno)mics in radiation therapy, the limitations of these techniques, and hurdles in introducing them into clinical practice are discussed. ©RSNA, 2024 Test Your Knowledge questions for this article are available in the supplemental material.
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Affiliation(s)
- Roberto García-Figueiras
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Sandra Baleato-González
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Antonio Luna
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Anwar R Padhani
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Joan C Vilanova
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Ana M Carballo-Castro
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Laura Oleaga-Zufiria
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Juan Antonio Vallejo-Casas
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Ana Marhuenda
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
| | - Antonio Gómez-Caamaño
- From the Department of Radiology, Division of Oncologic Imaging (R.G.F., S.B.G.), and Department of Radiation Oncology (A.M.C.C., A.G.C.), Hospital Clínico Universitario de Santiago de Compostela, Choupana s/n, 15706 Santiago de Compostela, Spain; Department of Advanced Medical Imaging, Grupo Health Time, Sercosa (Servicio Radiologia Computerizada, Clínica Las Nieves, Jaén, Spain (A.L.); Paul Strickland Scanner Centre, Mount Vernon Cancer Centre, Northwood, Middlesex, England (A.R.P.); Department of Radiology, Clínica Girona and Hospital Santa Caterina, Girona, Spain (J.C.V.); Department of Radiology, Hospital Clínic Barcelona, Barcelona, Spain (L.O.Z.); Unidad de Gestión Clínica de Medicina Nuclear, Instituto Maimónides de Investigación Biomédica de Córdoba, Hospital Universitario Reina Sofía, Córdoba, Spain (J.A.V.C.); and Department of Radiology, Instituto Valenciano de Oncología, Valencia, Spain (A.M.)
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Tham BZ, Aleman D, Nordström H, Nygren N, Coolens C. Plan Assessment Metrics for Dose Painting in Stereotactic Radiosurgery. Adv Radiat Oncol 2023; 8:101281. [PMID: 37415903 PMCID: PMC10320410 DOI: 10.1016/j.adro.2023.101281] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2022] [Accepted: 05/23/2023] [Indexed: 07/08/2023] Open
Abstract
Purpose As radiation therapy treatment precision increases with advancements in imaging and radiation delivery, dose painting treatment becomes increasingly feasible, where targets receive a nonuniform radiation dose. The high precision of stereotactic radiosurgery (SRS) makes it a good candidate for dose painting treatments, but no suitable metrics to assess dose painting SRS plans exist. Existing dose painting assessment metrics weigh target overdose and underdose equally but are unsuited for SRS plans, which typically avoid target underdose more. Current SRS metrics also prioritize reducing healthy tissue dose through selectivity and dose fall-off, and these metrics assume single prescriptions. We propose a set of metrics for dose painting SRS that would meet clinical needs and are calculated with nonuniform dose painting prescriptions. Methods and Materials Sample dose painting SRS prescriptions are first created from Gamma Knife SRS cases, apparent diffusion coefficient magnetic resonance images, and various image-to-prescription functions. Treatment plans are found through semi-infinite linear programming optimization and using clinically determined isocenters, then assessed with existing and proposed metrics. Modified versions of SRS metrics are proposed, including coverage, selectivity, conformity, efficiency, and gradient indices. Quality factor, a current dose painting metric, is applied both without changes and with modifications. A new metric, integral dose ratio, is proposed as a measure of target overdose. Results The merits of existing and modified metrics are demonstrated and discussed. A modified conformity index using mean or minimum prescription dose would be suitable for dose painting SRS with integral or maximum boost methods, respectively. Either modified efficiency index is a suitable replacement for the existing gradient index. Conclusions The proposed modified SRS metrics are appropriate measures of plan quality for dose painting SRS plans and have the advantage of giving equal values as the original SRS metrics when applied to single-prescription plans.
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Affiliation(s)
- Benjamin Z. Tham
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Dionne Aleman
- Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, Ontario, Canada
| | | | | | - Catherine Coolens
- Department of Radiation Oncology, Princess Margaret Cancer Centre, Toronto, Ontario, Canada
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5
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Jaffray DA, Knaul F, Baumann M, Gospodarowicz M. Harnessing progress in radiotherapy for global cancer control. NATURE CANCER 2023; 4:1228-1238. [PMID: 37749355 DOI: 10.1038/s43018-023-00619-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Accepted: 06/22/2023] [Indexed: 09/27/2023]
Abstract
The pace of technological innovation over the past three decades has transformed the field of radiotherapy into one of the most technologically intense disciplines in medicine. However, the global barriers to access this highly effective treatment are complex and extend beyond technological limitations. Here, we review the technological advancement and current status of radiotherapy and discuss the efforts of the global radiation oncology community to formulate a more integrative 'diagonal approach' in which the agendas of science-driven advances in individual outcomes and the sociotechnological task of global cancer control can be aligned to bring the benefit of this proven therapy to patients with cancer everywhere.
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Affiliation(s)
- David A Jaffray
- Departments of Radiation Physics and Imaging Physics, University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Felicia Knaul
- Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL, USA
| | | | - Mary Gospodarowicz
- Radiation Oncology, Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada
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6
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Aruah SC, Chidebe RC, Orjiakor TC, Uba F, Shagaya UN, Ugwanyi C, Umar AA, Ige T, Asogwa OC, Ahmadu OT, Ali-Gombe M, Adewumi A, Okwor VC, Mutiu JA, Bello B, Eriba LO, Ahmed YA, Bisalla A, Itanyi U, Balogun RA, Alabi S, Pistenmaa D, Coleman CN, Manjit D. Status of Government-Funded Radiotherapy Services in Nigeria. JCO Glob Oncol 2023; 9:e2200406. [PMID: 37348033 PMCID: PMC10497276 DOI: 10.1200/go.22.00406] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 02/20/2023] [Accepted: 04/18/2023] [Indexed: 06/24/2023] Open
Abstract
PURPOSE Access to radiotherapy (RT) is now one of the stark examples of global cancer inequities. More than 800,000 new cancer cases require potentially curative or palliative RT services in Africa, arguably <15% of these patients currently have access to this important service. For a population of more than 206 million, Nigeria requires a minimum of 280 RT machines for the increasing number of cancer cases. Painfully, the country has only eight Government-funded RT machines. This study aimed to evaluate the status of the eight Government-funded RT services in Nigeria and their ability to deliver effective RT to their patients. METHODS A survey addressing 10 critical areas was used to assess the eight Government-funded RT services in Nigeria. RESULTS Unfortunately, six of the eight centers (75%) surveyed have not treated patients with RT because they do not have functioning teletherapy machines in 2021. Only two RT centers have the capability of treating patients using advanced RT techniques. There is no positron emission tomography-computed tomography scan in any of the Government-funded RT centers. The workforce capacity and infrastructure across the eight centers are limited. All of the centers lack residency training programs for medical physicists and radiation therapy technologists resulting in very few well-trained staff. CONCLUSION As the Nigerian Government plans for the new National Cancer Control Plan, there is an urgent need to scale up access to RT by upgrading the RT equipment, workforce, and infrastructure to meet the current needs of Nigerian patients with cancer. Although the shortfall is apparent from a variety of RT-capacity databases, this detailed analysis provides essential information for an implementation plan involving solutions from within Nigeria and with global partners.
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Affiliation(s)
- Simeon C. Aruah
- Radiation Oncology Department, National Hospital Abuja, Abuja, Nigeria
- College of Medicine, University of Abuja, Gwagwalada, Abuja, Nigeria
| | - Runcie C.W. Chidebe
- Project PINK BLUE—Health & Psychological Trust Centre, Abuja, Nigeria
- Department of Sociology & Gerontology, Miami University, Oxford, OH
- Scripps Gerontology Center, Miami University, Oxford, OH
| | - Tochukwu C. Orjiakor
- Project PINK BLUE—Health & Psychological Trust Centre, Abuja, Nigeria
- Department of Psychology, University of Nigeria Nsukka, Enugu, Nigeria
| | - Fatima Uba
- Radiation and Clinical Oncology Department, National Hospital Abuja, Abuja, Nigeria
| | | | - Charles Ugwanyi
- Neurosurgery Department, National Hospital Abuja, Abuja, Nigeria
| | - Aisha A. Umar
- Department of Radiology, National Hospital Abuja, Abuja, Nigeria
| | - Taofeeq Ige
- Medical Physics Department, National Hospital Abuja, Abuja, Nigeria
| | - Obinna C. Asogwa
- Medical Physics Department, National Hospital Abuja, Abuja, Nigeria
- Radiotherapy Department, Asi Ukpo Comprehensive Cancer Center, Calabar, Nigeria
| | - Oiza T. Ahmadu
- Radiation Oncology Department, Ahmadu Bello University Teaching Hospital (ABUTH), Zaria, Nigeria
| | - Musa Ali-Gombe
- Radiology Department, Gombe State University, Gombe, Nigeria
- Department of Radiotherapy and Oncology, Federal Teaching Hospital Gombe, Gombe, Nigeria
| | - Alabi Adewumi
- Radiation Oncology Department, Nigeria Sovereign Investment Authority-Lagos University Teaching Hospital (NSIA-LUTH), Lagos, Nigeria
| | - Vitalis C. Okwor
- Radiation Oncology Department, University of Nigeria Teaching Hospital (UNTH), Ituku Ozalla, Enugu, Nigeria
| | - Jimoh A. Mutiu
- University of Ibadan/University College Hospital Ibadan, Ibadan, Nigeria
| | - Basheer Bello
- Radiation Oncology Department, Usman Danfodio University Teaching Hospital (UDUTH), Sokoto, Nigeria
| | - Lucy O. Eriba
- Radiation Oncology Department, University of Benin Teaching Hospital (UBTH), Edo State, Nigeria
| | - Yusuf A. Ahmed
- Centre for Energy Research and Training, Ahmadu Bello University, Zaria, Nigeria
- Nigeria Atomic Energy Commission (NAEC), Abuja, Nigeria
| | - Awwal Bisalla
- Nigeria Atomic Energy Commission (NAEC), Abuja, Nigeria
| | - Ukamaka Itanyi
- Radiology Department, University of Abuja Teaching Hospital (UATH), Gwagwalada, Abuja, Nigeria
| | | | - Suleiman Alabi
- Radiation and Clinical Oncology Department, National Hospital Abuja, Abuja, Nigeria
| | - David Pistenmaa
- International Cancer Expert Corps (ICEC) Inc, Washington, DC
| | | | - Dosanjh Manjit
- University of Oxford, Oxford, United Kingdom
- European Centre for Nuclear Research (CERN), Geneva, Switzerland
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7
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Sahlsten J, Wahid KA, Glerean E, Jaskari J, Naser MA, He R, Kann BH, Mäkitie A, Fuller CD, Kaski K. Segmentation stability of human head and neck cancer medical images for radiotherapy applications under de-identification conditions: Benchmarking data sharing and artificial intelligence use-cases. Front Oncol 2023; 13:1120392. [PMID: 36925936 PMCID: PMC10011442 DOI: 10.3389/fonc.2023.1120392] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2022] [Accepted: 02/13/2023] [Indexed: 03/08/2023] Open
Abstract
Background Demand for head and neck cancer (HNC) radiotherapy data in algorithmic development has prompted increased image dataset sharing. Medical images must comply with data protection requirements so that re-use is enabled without disclosing patient identifiers. Defacing, i.e., the removal of facial features from images, is often considered a reasonable compromise between data protection and re-usability for neuroimaging data. While defacing tools have been developed by the neuroimaging community, their acceptability for radiotherapy applications have not been explored. Therefore, this study systematically investigated the impact of available defacing algorithms on HNC organs at risk (OARs). Methods A publicly available dataset of magnetic resonance imaging scans for 55 HNC patients with eight segmented OARs (bilateral submandibular glands, parotid glands, level II neck lymph nodes, level III neck lymph nodes) was utilized. Eight publicly available defacing algorithms were investigated: afni_refacer, DeepDefacer, defacer, fsl_deface, mask_face, mri_deface, pydeface, and quickshear. Using a subset of scans where defacing succeeded (N=29), a 5-fold cross-validation 3D U-net based OAR auto-segmentation model was utilized to perform two main experiments: 1.) comparing original and defaced data for training when evaluated on original data; 2.) using original data for training and comparing the model evaluation on original and defaced data. Models were primarily assessed using the Dice similarity coefficient (DSC). Results Most defacing methods were unable to produce any usable images for evaluation, while mask_face, fsl_deface, and pydeface were unable to remove the face for 29%, 18%, and 24% of subjects, respectively. When using the original data for evaluation, the composite OAR DSC was statistically higher (p ≤ 0.05) for the model trained with the original data with a DSC of 0.760 compared to the mask_face, fsl_deface, and pydeface models with DSCs of 0.742, 0.736, and 0.449, respectively. Moreover, the model trained with original data had decreased performance (p ≤ 0.05) when evaluated on the defaced data with DSCs of 0.673, 0.693, and 0.406 for mask_face, fsl_deface, and pydeface, respectively. Conclusion Defacing algorithms may have a significant impact on HNC OAR auto-segmentation model training and testing. This work highlights the need for further development of HNC-specific image anonymization methods.
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Affiliation(s)
- Jaakko Sahlsten
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Kareem A. Wahid
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Enrico Glerean
- Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland
| | - Joel Jaskari
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
| | - Mohamed A. Naser
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Renjie He
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
| | - Benjamin H. Kann
- Artificial Intelligence in Medicine Program, Brigham and Women’s Hospital, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, United States
| | - Antti Mäkitie
- Department of Otorhinolaryngology, Head and Neck Surgery, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
| | - Clifton D. Fuller
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States
- *Correspondence: Clifton D. Fuller, ; Kimmo Kaski,
| | - Kimmo Kaski
- Department of Computer Science, Aalto University School of Science, Espoo, Finland
- *Correspondence: Clifton D. Fuller, ; Kimmo Kaski,
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8
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Chen X, Schädelin S, Lu PJ, Ocampo-Pineda M, Weigel M, Barakovic M, Ruberte E, Cagol A, Marechal B, Kober T, Kuhle J, Kappos L, Melie-Garcia L, Granziera C. Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients. Neuroimage Clin 2023; 37:103349. [PMID: 36801600 PMCID: PMC9958406 DOI: 10.1016/j.nicl.2023.103349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES AND AIMS Quantitative MRI (qMRI) has greatly improved the sensitivity and specificity of microstructural brain pathology in multiple sclerosis (MS) when compared to conventional MRI (cMRI). More than cMRI, qMRI also provides means to assess pathology within the normal-appearing and lesion tissue. In this work, we further developed a method providing personalized quantitative T1 (qT1) abnormality maps in individual MS patients by modeling the age dependence of qT1 alterations. In addition, we assessed the relationship between qT1 abnormality maps and patients' disability, in order to evaluate the potential value of this measurement in clinical practice. METHODS We included 119 MS patients (64 relapsing-remitting MS (RRMS), 34 secondary progressive MS (SPMS), 21 primary progressive MS (PPMS)), and 98 Healthy Controls (HC). All individuals underwent 3T MRI examinations, including Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) for qT1 maps and High-Resolution 3D Fluid Attenuated Inversion Recovery (FLAIR) imaging. To calculate personalized qT1 abnormality maps, we compared qT1 in each brain voxel in MS patients to the average qT1 obtained in the same tissue (grey/white matter) and region of interest (ROI) in healthy controls, hereby providing individual voxel-based Z-score maps. The age dependence of qT1 in HC was modeled using linear polynomial regression. We computed the average qT1 Z-scores in white matter lesions (WMLs), normal-appearing white matter (NAWM), cortical grey matter lesions (GMcLs) and normal-appearing cortical grey matter (NAcGM). Lastly, a multiple linear regression (MLR) model with the backward selection including age, sex, disease duration, phenotype, lesion number, lesion volume and average Z-score (NAWM/NAcGM/WMLs/GMcLs) was used to assess the relationship between qT1 measures and clinical disability (evaluated with EDSS). RESULTS The average qT1 Z-score was higher in WMLs than in NAWM. (WMLs: 1.366 ± 0.409, NAWM: -0.133 ± 0.288, [mean ± SD], p < 0.001). The average Z-score in NAWM in RRMS patients was significantly lower than in PPMS patients (p = 0.010). The MLR model showed a strong association between average qT1 Z-scores in white matter lesions (WMLs) and EDSS (R2 = 0.549, β = 0.178, 97.5 % CI = 0.030 to 0.326, p = 0.019). Specifically, we measured a 26.9 % increase in EDSS per unit of qT1 Z-score in WMLs in RRMS patients (R2 = 0.099, β = 0.269, 97.5 % CI = 0.078 to 0.461, p = 0.007). CONCLUSIONS We showed that personalized qT1 abnormality maps in MS patients provide measures related to clinical disability, supporting the use of those maps in clinical practice.
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Affiliation(s)
- Xinjie Chen
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Esther Ruberte
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benedicte Marechal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Jens Kuhle
- Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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9
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Volpe S, Isaksson LJ, Zaffaroni M, Pepa M, Raimondi S, Botta F, Lo Presti G, Vincini MG, Rampinelli C, Cremonesi M, de Marinis F, Spaggiari L, Gandini S, Guckenberger M, Orecchia R, Jereczek-Fossa BA. Impact of image filtering and assessment of volume-confounding effects on CT radiomic features and derived survival models in non-small cell lung cancer. Transl Lung Cancer Res 2022; 11:2452-2463. [PMID: 36636424 PMCID: PMC9830263 DOI: 10.21037/tlcr-22-248] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2022] [Accepted: 08/31/2022] [Indexed: 11/24/2022]
Abstract
Background No evidence supports the choice of specific imaging filtering methodologies in radiomics. As the volume of the primary tumor is a well-recognized prognosticator, our purpose is to assess how filtering may impact the feature/volume dependency in computed tomography (CT) images of non-small cell lung cancer (NSCLC), and if such impact translates into differences in the performance of survival modeling. The role of lesion volume in model performances was also considered and discussed. Methods Four-hundred seventeen CT images NSCLC patients were retrieved from the NSCLC-Radiomics public repository. Pre-processing and features extraction were implemented using Pyradiomics v3.0.1. Features showing high correlation with volume across original and filtered images were excluded. Cox proportional hazards (PH) with least absolute shrinkage and selection operator (LASSO) regularization and CatBoost models were built with and without volume, and their concordance (C-) indices were compared using Wilcoxon signed-ranked test. The Mann Whitney U test was used to assess model performances after stratification into two groups based on low- and high-volume lesions. Results Radiomic models significantly outperformed models built on only clinical variables and volume. However, the exclusion/inclusion of volume did not generally alter the performances of radiomic models. Overall, performances were not substantially affected by the choice of either imaging filter (overall C-index 0.539-0.590 for Cox PH and 0.589-0.612 for CatBoost). The separation of patients with high-volume lesions resulted in significantly better performances in 2/10 and 7/10 cases for Cox PH and CatBoost models, respectively. Both low- and high-volume models performed significantly better with the inclusion of radiomic features (P<0.0001), but the improvement was largest in the high-volume group (+10.2% against +8.7% improvement for CatBoost models and +10.0% against +5.4% in Cox PH models). Conclusions Radiomic features complement well-known prognostic factors such as volume, but their volume-dependency is high and should be managed with vigilance. The informative content of radiomic features may be diminished in small lesion volumes, which could limit the applicability of radiomics in early-stage NSCLC, where tumors tend to be small. Our results also suggest an advantage of CatBoost models over the Cox PH models.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | | | - Mattia Zaffaroni
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Raimondi
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Francesca Botta
- Medical Physics Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Giuliana Lo Presti
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Maria Giulia Vincini
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Cristiano Rampinelli
- Department of Radiology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Marta Cremonesi
- Radiation Research Unit, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Filippo de Marinis
- Division of Thoracic Oncology, European Institute of Oncology, IRCCS, Milan, Italy
| | - Lorenzo Spaggiari
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy;,Division of Thoracic Surgery, European Institute of Oncology IRCCS, Milan, Italy
| | - Sara Gandini
- Department of Experimental Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Matthias Guckenberger
- Department of Radiation Oncology, University Hospital Zurich, University of Zurich, Zurich, Switzerland
| | - Roberto Orecchia
- Scientific Direction, IEO, European Institute of Oncology IRCCS, Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, IEO, European Institute of Oncology IRCCS, Milan, Italy;,Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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10
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Artificial Intelligence for Outcome Modeling in Radiotherapy. Semin Radiat Oncol 2022; 32:351-364. [DOI: 10.1016/j.semradonc.2022.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
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11
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Hubbard Cristinacce PL, Keaveney S, Aboagye EO, Hall MG, Little RA, O'Connor JPB, Parker GJM, Waterton JC, Winfield JM, Jauregui-Osoro M. Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice. Phys Med 2022; 101:165-182. [PMID: 36055125 DOI: 10.1016/j.ejmp.2022.08.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/05/2022] [Accepted: 08/17/2022] [Indexed: 10/14/2022] Open
Abstract
PURPOSE This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice. METHODS We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK. RESULTS The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions. CONCLUSIONS We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.
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Affiliation(s)
| | - Sam Keaveney
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Eric O Aboagye
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
| | - Matt G Hall
- National Physical Laboratory, Hampton Road, Teddington TW11 0LW, UK
| | - Ross A Little
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - James P B O'Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Geoff J M Parker
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, 90 High Holborn, London WC1V 6LJ, UK; Bioxydyn Ltd, Manchester M15 6SZ, UK
| | - John C Waterton
- Bioxydyn Ltd, Manchester M15 6SZ, UK; Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Jessica M Winfield
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Maite Jauregui-Osoro
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
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12
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Abdollahi H, Chin E, Clark H, Hyde DE, Thomas S, Wu J, Uribe CF, Rahmim A. Radiomics-guided radiation therapy: opportunities and challenges. Phys Med Biol 2022; 67. [DOI: 10.1088/1361-6560/ac6fab] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Accepted: 05/13/2022] [Indexed: 11/11/2022]
Abstract
Abstract
Radiomics is an advanced image-processing framework, which extracts image features and considers them as biomarkers towards personalized medicine. Applications include disease detection, diagnosis, prognosis, and therapy response assessment/prediction. As radiation therapy aims for further individualized treatments, radiomics could play a critical role in various steps before, during and after treatment. Elucidation of the concept of radiomics-guided radiation therapy (RGRT) is the aim of this review, attempting to highlight opportunities and challenges underlying the use of radiomics to guide clinicians and physicists towards more effective radiation treatments. This work identifies the value of RGRT in various steps of radiotherapy from patient selection to follow-up, and subsequently provides recommendations to improve future radiotherapy using quantitative imaging features.
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13
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Lefebvre TL, Brown E, Hacker L, Else T, Oraiopoulou ME, Tomaszewski MR, Jena R, Bohndiek SE. The Potential of Photoacoustic Imaging in Radiation Oncology. Front Oncol 2022; 12:803777. [PMID: 35311156 PMCID: PMC8928467 DOI: 10.3389/fonc.2022.803777] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 02/07/2022] [Indexed: 12/16/2022] Open
Abstract
Radiotherapy is recognized globally as a mainstay of treatment in most solid tumors and is essential in both curative and palliative settings. Ionizing radiation is frequently combined with surgery, either preoperatively or postoperatively, and with systemic chemotherapy. Recent advances in imaging have enabled precise targeting of solid lesions yet substantial intratumoral heterogeneity means that treatment planning and monitoring remains a clinical challenge as therapy response can take weeks to manifest on conventional imaging and early indications of progression can be misleading. Photoacoustic imaging (PAI) is an emerging modality for molecular imaging of cancer, enabling non-invasive assessment of endogenous tissue chromophores with optical contrast at unprecedented spatio-temporal resolution. Preclinical studies in mouse models have shown that PAI could be used to assess response to radiotherapy and chemoradiotherapy based on changes in the tumor vascular architecture and blood oxygen saturation, which are closely linked to tumor hypoxia. Given the strong relationship between hypoxia and radio-resistance, PAI assessment of the tumor microenvironment has the potential to be applied longitudinally during radiotherapy to detect resistance at much earlier time-points than currently achieved by size measurements and tailor treatments based on tumor oxygen availability and vascular heterogeneity. Here, we review the current state-of-the-art in PAI in the context of radiotherapy research. Based on these studies, we identify promising applications of PAI in radiation oncology and discuss the future potential and outstanding challenges in the development of translational PAI biomarkers of early response to radiotherapy.
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Affiliation(s)
- Thierry L. Lefebvre
- Department of Physics, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Emma Brown
- Department of Physics, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Lina Hacker
- Department of Physics, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Thomas Else
- Department of Physics, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Mariam-Eleni Oraiopoulou
- Department of Physics, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
| | - Michal R. Tomaszewski
- Department of Cancer Physiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL, United States
| | - Rajesh Jena
- Department of Oncology, University of Cambridge, Cambridge, United Kingdom
| | - Sarah E. Bohndiek
- Department of Physics, University of Cambridge, Cambridge, United Kingdom
- Cancer Research UK Cambridge Institute, University of Cambridge, Cambridge, United Kingdom
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14
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Li M, Zhang Q, Yang K. Role of MRI-Based Functional Imaging in Improving the Therapeutic Index of Radiotherapy in Cancer Treatment. Front Oncol 2021; 11:645177. [PMID: 34513659 PMCID: PMC8429950 DOI: 10.3389/fonc.2021.645177] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2020] [Accepted: 07/30/2021] [Indexed: 02/05/2023] Open
Abstract
Advances in radiation technology, such as intensity-modulated radiation therapy (IMRT), have largely enabled a biological dose escalation of the target volume (TV) and reduce the dose to adjacent tissues or organs at risk (OARs). However, the risk of radiation-induced injury increases as more radiation dose utilized during radiation therapy (RT), which predominantly limits further increases in TV dose distribution and reduces the local control rate. Thus, the accurate target delineation is crucial. Recently, technological improvements for precise target delineation have obtained more attention in the field of RT. The addition of functional imaging to RT can provide a more accurate anatomy of the tumor and normal tissues (such as location and size), along with biological information that aids to optimize the therapeutic index (TI) of RT. In this review, we discuss the application of some common MRI-based functional imaging techniques in clinical practice. In addition, we summarize the main challenges and prospects of these imaging technologies, expecting more inspiring developments and more productive research paths in the near future.
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Affiliation(s)
- Mei Li
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China.,West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Qin Zhang
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Kaixuan Yang
- Department of Gynecology and Obstetrics, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, West China Second University Hospital, Sichuan University, Chengdu, China
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Nguyen AAT, Arasu VA, Strand F, Li W, Onishi N, Gibbs J, Jones EF, Joe BN, Esserman LJ, Newitt DC, Hylton NM. Comparison of Segmentation Methods in Assessing Background Parenchymal Enhancement as a Biomarker for Response to Neoadjuvant Therapy. ACTA ACUST UNITED AC 2021; 6:101-110. [PMID: 32548286 PMCID: PMC7289261 DOI: 10.18383/j.tom.2020.00009] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Breast parenchymal enhancement (BPE) has shown association with breast cancer risk and response to neoadjuvant treatment. However, BPE quantification is challenging, and there is no standardized segmentation method for measurement. We investigated the use of a fully automated breast fibroglandular tissue segmentation method to calculate BPE from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for use as a predictor of pathologic complete response (pCR) following neoadjuvant treatment in the I-SPY 2 TRIAL. In this trial, patients had DCE-MRI at baseline (T0), after 3 weeks of treatment (T1), after 12 weeks of treatment and between drug regimens (T2), and after completion of treatment (T3). A retrospective analysis of 2 cohorts was performed: one with 735 patients and another with a final cohort of 340 patients, meeting a high-quality benchmark for segmentation. We evaluated 3 subvolumes of interest segmented from bilateral T1-weighted axial breast DCE-MRI: full stack (all axial slices), half stack (center 50% of slices), and center 5 slices. The differences between methods were assessed, and a univariate logistic regression model was implemented to determine the predictive performance of each segmentation method. The results showed that the half stack method provided the best compromise between sampling error from too little tissue and inclusion of incorrectly segmented tissues from extreme superior and inferior regions. Our results indicate that BPE calculated using the half stack segmentation approach has potential as an early biomarker for response to treatment in the hormone receptor–negative and human epidermal growth factor receptor 2–positive subtype.
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Affiliation(s)
- Alex Anh-Tu Nguyen
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Vignesh A Arasu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA.,Department of Radiology, Kaiser Permanente Medical Center, Vallejo, CA
| | - Fredrik Strand
- Department of Oncology and Pathology, Karolinska Institute, Stockholm, Sweden; and
| | - Wen Li
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Natsuko Onishi
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Jessica Gibbs
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Ella F Jones
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Bonnie N Joe
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Laura J Esserman
- Department of Surgery, University of California, San Francisco, San Francisco, CA
| | - David C Newitt
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
| | - Nola M Hylton
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA
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Hricak H, Abdel-Wahab M, Atun R, Lette MM, Paez D, Brink JA, Donoso-Bach L, Frija G, Hierath M, Holmberg O, Khong PL, Lewis JS, McGinty G, Oyen WJG, Shulman LN, Ward ZJ, Scott AM. Medical imaging and nuclear medicine: a Lancet Oncology Commission. Lancet Oncol 2021; 22:e136-e172. [PMID: 33676609 PMCID: PMC8444235 DOI: 10.1016/s1470-2045(20)30751-8] [Citation(s) in RCA: 103] [Impact Index Per Article: 34.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Revised: 12/04/2020] [Accepted: 12/07/2020] [Indexed: 12/13/2022]
Abstract
The diagnosis and treatment of patients with cancer requires access to imaging to ensure accurate management decisions and optimal outcomes. Our global assessment of imaging and nuclear medicine resources identified substantial shortages in equipment and workforce, particularly in low-income and middle-income countries (LMICs). A microsimulation model of 11 cancers showed that the scale-up of imaging would avert 3·2% (2·46 million) of all 76·0 million deaths caused by the modelled cancers worldwide between 2020 and 2030, saving 54·92 million life-years. A comprehensive scale-up of imaging, treatment, and care quality would avert 9·55 million (12·5%) of all cancer deaths caused by the modelled cancers worldwide, saving 232·30 million life-years. Scale-up of imaging would cost US$6·84 billion in 2020-30 but yield lifetime productivity gains of $1·23 trillion worldwide, a net return of $179·19 per $1 invested. Combining the scale-up of imaging, treatment, and quality of care would provide a net benefit of $2·66 trillion and a net return of $12·43 per $1 invested. With the use of a conservative approach regarding human capital, the scale-up of imaging alone would provide a net benefit of $209·46 billion and net return of $31·61 per $1 invested. With comprehensive scale-up, the worldwide net benefit using the human capital approach is $340·42 billion and the return per dollar invested is $2·46. These improved health and economic outcomes hold true across all geographical regions. We propose actions and investments that would enhance access to imaging equipment, workforce capacity, digital technology, radiopharmaceuticals, and research and training programmes in LMICs, to produce massive health and economic benefits and reduce the burden of cancer globally.
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Affiliation(s)
- Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Department of Radiology, Weill Cornell Medical College, New York, NY, USA.
| | - May Abdel-Wahab
- International Atomic Energy Agency, Division of Human Health, Vienna, Austria; Radiation Oncology, National Cancer Institute, Cairo University, Cairo, Egypt; Graduate School of Biomedical and Health Sciences, Hiroshima University, Hiroshima, Japan
| | - Rifat Atun
- Department of Global Health and Population, Harvard TH Chan School of Public Health, Boston, MA, USA; Department of Global Health and Social Medicine, Harvard Medical School, Harvard University, Boston, MA, USA
| | | | - Diana Paez
- International Atomic Energy Agency, Division of Human Health, Vienna, Austria
| | - James A Brink
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Harvard University, Boston, MA, USA
| | - Lluís Donoso-Bach
- Department of Medical Imaging, Hospital Clínic of Barcelona, University of Barcelona, Barcelona, Spain
| | | | | | - Ola Holmberg
- Radiation Protection of Patients Unit, International Atomic Energy Agency, Vienna, Austria
| | - Pek-Lan Khong
- Department of Diagnostic Radiology, University of Hong Kong, Hong Kong Special Administrative Region, China
| | - Jason S Lewis
- Department of Radiology and Molecular Pharmacology Programme, Memorial Sloan Kettering Cancer Center, New York, NY, USA; Departments of Pharmacology and Radiology, Weill Cornell Medical College, New York, NY, USA
| | - Geraldine McGinty
- Departments of Radiology and Population Science, Weill Cornell Medical College, New York, NY, USA; American College of Radiology, Reston, VA, USA
| | - Wim J G Oyen
- Department of Biomedical Sciences and Humanitas Clinical and Research Centre, Department of Nuclear Medicine, Humanitas University, Milan, Italy; Department of Radiology and Nuclear Medicine, Rijnstate Hospital, Arnhem, Netherlands; Department of Radiology and Nuclear Medicine, Radboud University Medical Centre, Nijmegen, Netherlands
| | - Lawrence N Shulman
- Department of Medicine, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA, USA
| | - Zachary J Ward
- Center for Health Decision Science, Harvard TH Chan School of Public Health, Boston, MA, USA
| | - Andrew M Scott
- Tumour Targeting Laboratory, Olivia Newton-John Cancer Research Institute, Melbourne, VIC, Australia; Department of Molecular Imaging and Therapy, Austin Health, Melbourne, VIC, Australia; School of Cancer Medicine, La Trobe University, Melbourne, VIC, Australia; Department of Medicine, University of Melbourne, Melbourne, VIC, Australia
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17
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van Houdt PJ, Yang Y, van der Heide UA. Quantitative Magnetic Resonance Imaging for Biological Image-Guided Adaptive Radiotherapy. Front Oncol 2021; 10:615643. [PMID: 33585242 PMCID: PMC7878523 DOI: 10.3389/fonc.2020.615643] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/09/2020] [Accepted: 12/08/2020] [Indexed: 12/20/2022] Open
Abstract
MRI-guided radiotherapy systems have the potential to bring two important concepts in modern radiotherapy together: adaptive radiotherapy and biological targeting. Based on frequent anatomical and functional imaging, monitoring the changes that occur in volume, shape as well as biological characteristics, a treatment plan can be updated regularly to accommodate the observed treatment response. For this purpose, quantitative imaging biomarkers need to be identified that show changes early during treatment and predict treatment outcome. This review provides an overview of the current evidence on quantitative MRI measurements during radiotherapy and their potential as an imaging biomarker on MRI-guided radiotherapy systems.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
| | - Yingli Yang
- Department of Radiation Oncology, University of California, Los Angeles, CA, United States
| | - Uulke A van der Heide
- Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam, Netherlands
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Shirvani SM, Huntzinger CJ, Melcher T, Olcott PD, Voronenko Y, Bartlett-Roberto J, Mazin S. Biology-guided radiotherapy: redefining the role of radiotherapy in metastatic cancer. Br J Radiol 2021; 94:20200873. [PMID: 33112685 PMCID: PMC7774706 DOI: 10.1259/bjr.20200873] [Citation(s) in RCA: 33] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/17/2020] [Revised: 09/30/2020] [Accepted: 10/05/2020] [Indexed: 12/14/2022] Open
Abstract
The emerging biological understanding of metastatic cancer and proof-of-concept clinical trials suggest that debulking all gross disease holds great promise for improving patient outcomes. However, ablation of multiple targets with conventional external beam radiotherapy systems is burdensome, which limits investigation and utilization of complete metastatic ablation in the majority of patients with advanced disease. To overcome this logistical hurdle, technical innovation is necessary. Biology-guided radiotherapy (BgRT) is a new external beam radiotherapy delivery modality combining positron emission tomography-computed tomography (PET-CT) with a 6 MV linear accelerator. The key innovation is continuous response of the linear accelerator to outgoing tumor PET emissions with beamlets of radiotherapy at subsecond latency. This allows the deposited dose to track tumors in real time. Multiple new hardware and algorithmic advances further facilitate this low-latency feedback process. By transforming tumors into their own fiducials after intravenous injection of a radiotracer, BgRT has the potential to enable complete metastatic ablation in a manner efficient for a single patient and scalable to entire populations with metastatic disease. Future trends may further enhance the utility of BgRT in the clinic as this technology dovetails with other innovations in radiotherapy, including novel dose painting and fractionation schemes, radiomics, and new radiotracers.
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Otazo R, Lambin P, Pignol JP, Ladd ME, Schlemmer HP, Baumann M, Hricak H. MRI-guided Radiation Therapy: An Emerging Paradigm in Adaptive Radiation Oncology. Radiology 2020; 298:248-260. [PMID: 33350894 DOI: 10.1148/radiol.2020202747] [Citation(s) in RCA: 69] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
Radiation therapy (RT) continues to be one of the mainstays of cancer treatment. Considerable efforts have been recently devoted to integrating MRI into clinical RT planning and monitoring. This integration, known as MRI-guided RT, has been motivated by the superior soft-tissue contrast, organ motion visualization, and ability to monitor tumor and tissue physiologic changes provided by MRI compared with CT. Offline MRI is already used for treatment planning at many institutions. Furthermore, MRI-guided linear accelerator systems, allowing use of MRI during treatment, enable improved adaptation to anatomic changes between RT fractions compared with CT guidance. Efforts are underway to develop real-time MRI-guided intrafraction adaptive RT of tumors affected by motion and MRI-derived biomarkers to monitor treatment response and potentially adapt treatment to physiologic changes. These developments in MRI guidance provide the basis for a paradigm change in treatment planning, monitoring, and adaptation. Key challenges to advancing MRI-guided RT include real-time volumetric anatomic imaging, addressing image distortion because of magnetic field inhomogeneities, reproducible quantitative imaging across different MRI systems, and biologic validation of quantitative imaging. This review describes emerging innovations in offline and online MRI-guided RT, exciting opportunities they offer for advancing research and clinical care, hurdles to be overcome, and the need for multidisciplinary collaboration.
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Affiliation(s)
- Ricardo Otazo
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Philippe Lambin
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Jean-Philippe Pignol
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Mark E Ladd
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Heinz-Peter Schlemmer
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Michael Baumann
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
| | - Hedvig Hricak
- From the Departments of Medical Physics (R.O.) and Radiology (R.O., H.H.), Memorial Sloan-Kettering Cancer Center, 1275 York Ave, New York, NY 10065; The D-Lab, Department of Precision Medicine, Department of Radiology & Nuclear Medicine, GROW-School for Oncology, Maastricht University Medical Centre, Maastricht, the Netherlands (P.L.); Department of Radiation Oncology, Dalhousie University, Halifax, Canada (J.P.P.); Divisions of Medical Physics in Radiology (M.E.L.), Radiology (H.P.S.), and Radiation Oncology/Radiobiology (M.B.), German Cancer Research Center (DKFZ), Heidelberg, Germany; Faculty of Physics and Astronomy (M.E.L.) and Faculty of Medicine (M.E.L., M.B.), Heidelberg University, Heidelberg, Germany; and OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine and University Hospital Carl Gustav Carus, Technische Universität Dresden, Helmholtz-Zentrum Dresden-Rossendorf, Dresden, Germany (M.B.)
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20
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Scholey JE, Chandramohan D, Naren T, Liu W, Larson PEZ, Sudhyadhom A. Technical Note: A methodology for improved accuracy in stopping power estimation using MRI and CT. Med Phys 2020; 48:342-353. [PMID: 33107997 DOI: 10.1002/mp.14555] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Revised: 09/21/2020] [Accepted: 10/19/2020] [Indexed: 01/13/2023] Open
Abstract
PURPOSE Proton therapy is becoming an increasingly popular cancer treatment modality due to the proton's physical advantage in that it deposits the majority of its energy at the distal end of its track where the tumor is located. The proton range in a material is determined from the stopping power ratio (SPR) of the material. However, SPR is typically estimated based on a computed tomography (CT) scan which can lead to range estimation errors due to the difference in x-ray and proton interactions in matter, which can preclude the ability to utilize protons to their full potential. Applications of magnetic resonance imaging (MRI) in radiotherapy have increased over the past decade and using MRI to calculate SPR directly could provide numerous advantages. The purpose of this study was to develop a practical implementation of a novel multimodal imaging method for estimating SPR and compare the results of this method to physical measurements in which values were computed directly using tissue substitute materials fabricated to mimic skin, muscle, adipose, and spongiosa bone. METHODS For both the multimodal imaging method and physical measurements, SPR was calculated using the Bethe-Bloch equation from values of relative electron density and mean ionization potential determined for each tissue. Parameters used to estimate SPR using the multimodal imaging method were extracted from Dixon water-only and (1 H) proton density-weighted zero echo time MRI sequences and CT, with both kVCT and MVCT used separately to evaluate the performance of each. For comparison, SPR was also computed from kVCT using the stoichiometric method, the current clinical standard. RESULTS Results showed that our multimodal imaging approach using MRI with either kVCT or MVCT was in close agreement to SPR calculated from physical measurements for the four tissue substitutes evaluated. Using MRI and MVCT, SPR values estimated using our method were within 1% of physical measurements and were more accurate than the stoichiometric method for the tissue types studied. CONCLUSIONS We have demonstrated the methodology for improved estimation of SPR using the proposed multimodal imaging framework.
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Affiliation(s)
- Jessica E Scholey
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
| | - Dharshan Chandramohan
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
| | - Tarun Naren
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
| | - William Liu
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
| | - Peder Eric Zufall Larson
- Department of Radiology and Biomedical Imaging, The University of California, San Francisco, CA, USA
| | - Atchar Sudhyadhom
- Department of Radiation Oncology, The University of California, San Francisco, CA, USA
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van Houdt PJ, Kallehauge JF, Tanderup K, Nout R, Zaletelj M, Tadic T, van Kesteren ZJ, van den Berg CAT, Georg D, Côté JC, Levesque IR, Swamidas J, Malinen E, Telliskivi S, Brynolfsson P, Mahmood F, van der Heide UA. Phantom-based quality assurance for multicenter quantitative MRI in locally advanced cervical cancer. Radiother Oncol 2020; 153:114-121. [PMID: 32931890 DOI: 10.1016/j.radonc.2020.09.013] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2020] [Revised: 09/07/2020] [Accepted: 09/08/2020] [Indexed: 02/08/2023]
Abstract
BACKGROUND AND PURPOSE A wide variation of MRI systems is a challenge in multicenter imaging biomarker studies as it adds variation in quantitative MRI values. The aim of this study was to design and test a quality assurance (QA) framework based on phantom measurements, for the quantitative MRI protocols of a multicenter imaging biomarker trial of locally advanced cervical cancer. MATERIALS AND METHODS Fifteen institutes participated (five 1.5 T and ten 3 T scanners). Each institute optimized protocols for T2, diffusion-weighted imaging, T1, and dynamic contrast-enhanced (DCE-)MRI according to system possibilities, institutional preferences and study-specific constraints. Calibration phantoms with known values were used for validation. Benchmark protocols, similar on all systems, were used to investigate whether differences resulted from variations in institutional protocols or from system variations. Bias, repeatability (%RC), and reproducibility (%RDC) were determined. Ratios were used for T2 and T1 values. RESULTS The institutional protocols showed a range in bias of 0.88-0.98 for T2 (median %RC = 1%; %RDC = 12%), -0.007 to 0.029 × 10-3 mm2/s for the apparent diffusion coefficient (median %RC = 3%; %RDC = 18%), and 0.39-1.29 for T1 (median %RC = 1%; %RDC = 33%). For DCE a nonlinear vendor-specific relation was observed between measured and true concentrations with magnitude data, whereas the relation was linear when phase data was used. CONCLUSION We designed a QA framework for quantitative MRI protocols and demonstrated for a multicenter trial for cervical cancer that measurement of consistent T2 and apparent diffusion coefficient values is feasible despite protocol differences. For DCE-MRI and T1 mapping with the variable flip angle method, this was more challenging.
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Affiliation(s)
- Petra J van Houdt
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands.
| | | | - Kari Tanderup
- Department of Clinical Medicine, Aarhus University Hospital, Denmark
| | - Remi Nout
- Department of Radiation Oncology, Leiden University Medical Center, the Netherlands
| | - Marko Zaletelj
- Department of Radiotherapy, Institute of Oncology Ljubljana, Slovenia
| | - Tony Tadic
- Radiation Medicine Program, Princess Margaret Cancer Center, Toronto, Canada
| | - Zdenko J van Kesteren
- Department of Radiation Oncology, Amsterdam University Medical Center, the Netherlands
| | | | - Dietmar Georg
- Division of Medical Radiation Physics, Department of Radiation Oncology, Medical University Of Vienna, Austria
| | - Jean-Charles Côté
- Department of Radiation Oncology, Centre Hospitalier de l'Universite de Montreal, Canada
| | - Ives R Levesque
- Medical Physics Unit and Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada
| | - Jamema Swamidas
- Department of Radiation Oncology, Tata Memorial Centre, Mumbai, India
| | - Eirik Malinen
- Department of Medical Physics, Oslo University Hospital, Norway
| | - Sven Telliskivi
- Department of Radiation Oncology, North-Estonia Medical Centre, Tallinn, Estonia
| | - Patrik Brynolfsson
- Department of Translational Sciences, Skåne University Hospital, Lund, Sweden
| | - Faisal Mahmood
- Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, the Netherlands
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Magnetic resonance imaging-derived radiomic signature predicts locoregional failure after organ preservation therapy in patients with hypopharyngeal squamous cell carcinoma. Clin Transl Radiat Oncol 2020; 25:1-9. [PMID: 33426314 PMCID: PMC7780126 DOI: 10.1016/j.ctro.2020.08.004] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 08/16/2020] [Accepted: 08/24/2020] [Indexed: 01/08/2023] Open
Abstract
The first study to develop and validate an MRI-derived radiomic signature (RS) for the prediction of 1-year locoregional failure in HPSCC patients receiving OPT. The RS-based model (RS of 0.0326 as the cut-off value) provides a novel and convenient approach for the prediction of the 1-year clinical outcomes. The proposed RS-based model can help physicians characterize and facilitate decision-making for the clinical management of patients with locally advanced HPSCC.
Background and purpose To develop and validate a magnetic resonance imaging (MRI)-derived radiomic signature (RS) for the prediction of 1-year locoregional failure (LRF) in patients with hypopharyngeal squamous cell carcinoma (HPSCC) who received organ preservation therapy (OPT) Material and methods A total of 800 MRI-based features of pretreatment tumors were obtained from 116 patients with HPSCC who received OPT from two independent cohorts. The least absolute shrinkage and selection operator regression model were used to select the features used to develop the RS. Harrell’s C-index and corrected C-index were used to evaluate the discriminative ability of RS. The Youden index was used to select the optimal cut-point for risk category. Results The RS yielded 1000 times bootstrapping corrected C-index of 0.8036 and 0.78235 in the experimental (n = 82) and validation cohorts (n = 34), respectively. With respect to the subgroup of patients with stage III/IV and cT4 disease, the RS also showed good predictive performance with corrected C-indices of 0.760 and 0.754, respectively. The dichotomized risk category using an RS of 0.0326 as the cut-off value yielded a 1-year LRF predictive accuracy of 79.27%, 79.41%, 76.74%, and 71.15% in the experimental, validation, stage III/IV, and cT4a cohorts, respectively. The low-risk group was associated with a significantly better progression-free laryngectomy-free and overall survival outcome in two independent institutions, stage III/IV, and cT4a cohorts. Conclusion The RS-based model provides a novel and convenient approach for the prediction of the 1-year LRF and survival outcome in patients with HPSCC who received OPT.
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Shukla M, Forghani R, Agarwal M. Patient-Centric Head and Neck Cancer Radiation Therapy: Role of Advanced Imaging. Neuroimaging Clin N Am 2020; 30:341-357. [PMID: 32600635 DOI: 10.1016/j.nic.2020.04.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The traditional 'one-size-fits-all' approach to H&N cancer therapy is archaic. Advanced imaging can identify radioresistant areas by using biomarkers that detect tumor hypoxia, hypercellularity etc. Highly conformal radiotherapy can target resistant areas with precision. The critical information that can be gleaned about tumor biology from these advanced imaging modalities facilitates individualized radiotherapy. The tumor imaging world is pushing its boundaries. Molecular imaging can now detect protein expression and genotypic variations across tumors that can be exploited for tailoring treatment. The exploding field of radiomics and radiogenomics extracts quantitative, biologic and genetic information and further expands the scope of personalized therapy.
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Affiliation(s)
- Monica Shukla
- Department of Radiation Oncology, Froedtert and Medical College of Wisconsin, 9200 W. Wisconsin Avenue, Milwaukee, WI 53226, USA
| | - Reza Forghani
- Augmented Intelligence & Precision Health Laboratory, Department of Radiology, Research Institute of McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec H4A 3J1, Canada
| | - Mohit Agarwal
- Department of Radiology, Section of Neuroradiology, Froedtert and Medical College of Wisconsin, 8701 Watertown Plank Road, Milwaukee, WI 53226, USA.
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24
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Jones EF, Buatti JM, Shu HK, Wahl RL, Kurland BF, Linden HM, Mankoff DA, Rubin DL, Tata D, Nordstrom RJ, Hadjiyski L, Holdhoff M, Schwartz LH. Clinical Trial Design and Development Work Group Within the Quantitative Imaging Network. Tomography 2020; 6:60-64. [PMID: 32548281 PMCID: PMC7289239 DOI: 10.18383/j.tom.2019.00022] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
The Clinical Trial Design and Development Working Group within the Quantitative Imaging Network focuses on providing support for the development, validation, and harmonization of quantitative imaging (QI) methods and tools for use in cancer clinical trials. In the past 10 years, the Group has been working in several areas to identify challenges and opportunities in clinical trials involving QI and radiation oncology. The Group has been working with Quantitative Imaging Network members and the Quantitative Imaging Biomarkers Alliance leadership to develop guidelines for standardizing the reporting of quantitative imaging. As a validation platform, the Group led a multireader study to test a semi-automated positron emission tomography quantification software. Clinical translation of QI tools cannot be possible without a continuing dialogue with clinical users. This article also highlights the outreach activities extended to cooperative groups and other organizations that promote the use of QI tools to support clinical decisions.
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Affiliation(s)
- Ella F. Jones
- School of Medicine, University of California San Francisco, San Francisco, CA
| | - John M. Buatti
- Carver College of Medicine, The University of Iowa, Iowa City, IA
| | - Hui-Kuo Shu
- Winship Cancer Institute, Emory University, Atlanta, GA
| | | | - Brenda F. Kurland
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA
- School of Medicine, University of Washington, Seattle, WA
| | | | - David A. Mankoff
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Darrell Tata
- Cancer Imaging Program, National Cancer Institute, Bethesda, MD
| | | | | | - Matthias Holdhoff
- Sidney Kimmel Comprehensive Cancer Center, John Hopkins University, Baltimore, MD; and
| | - Lawrence H. Schwartz
- Irving Medical Center, Columbia University, New York Presbyterian Hospital, New York, NY
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25
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Kurz C, Buizza G, Landry G, Kamp F, Rabe M, Paganelli C, Baroni G, Reiner M, Keall PJ, van den Berg CAT, Riboldi M. Medical physics challenges in clinical MR-guided radiotherapy. Radiat Oncol 2020; 15:93. [PMID: 32370788 PMCID: PMC7201982 DOI: 10.1186/s13014-020-01524-4] [Citation(s) in RCA: 77] [Impact Index Per Article: 19.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2019] [Accepted: 03/24/2020] [Indexed: 12/18/2022] Open
Abstract
The integration of magnetic resonance imaging (MRI) for guidance in external beam radiotherapy has faced significant research and development efforts in recent years. The current availability of linear accelerators with an embedded MRI unit, providing volumetric imaging at excellent soft tissue contrast, is expected to provide novel possibilities in the implementation of image-guided adaptive radiotherapy (IGART) protocols. This study reviews open medical physics issues in MR-guided radiotherapy (MRgRT) implementation, with a focus on current approaches and on the potential for innovation in IGART.Daily imaging in MRgRT provides the ability to visualize the static anatomy, to capture internal tumor motion and to extract quantitative image features for treatment verification and monitoring. Those capabilities enable the use of treatment adaptation, with potential benefits in terms of personalized medicine. The use of online MRI requires dedicated efforts to perform accurate dose measurements and calculations, due to the presence of magnetic fields. Likewise, MRgRT requires dedicated quality assurance (QA) protocols for safe clinical implementation.Reaction to anatomical changes in MRgRT, as visualized on daily images, demands for treatment adaptation concepts, with stringent requirements in terms of fast and accurate validation before the treatment fraction can be delivered. This entails specific challenges in terms of treatment workflow optimization, QA, and verification of the expected delivered dose while the patient is in treatment position. Those challenges require specialized medical physics developments towards the aim of fully exploiting MRI capabilities. Conversely, the use of MRgRT allows for higher confidence in tumor targeting and organs-at-risk (OAR) sparing.The systematic use of MRgRT brings the possibility of leveraging IGART methods for the optimization of tumor targeting and quantitative treatment verification. Although several challenges exist, the intrinsic benefits of MRgRT will provide a deeper understanding of dose delivery effects on an individual basis, with the potential for further treatment personalization.
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Affiliation(s)
- Christopher Kurz
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
| | - Giulia Buizza
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guillaume Landry
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany
- German Cancer Consortium (DKTK), 81377, Munich, Germany
| | - Florian Kamp
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Moritz Rabe
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Chiara Paganelli
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
| | - Guido Baroni
- Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.za Leonardo da Vinci 32, 20133, Milano, Italy
- Bioengineering Unit, National Center of Oncological Hadrontherapy (CNAO), Strada Privata Campeggi 53, 27100, Pavia, Italy
| | - Michael Reiner
- Department of Radiation Oncology, University Hospital, LMU Munich, Marchioninistraße 15, 81377, Munich, Germany
| | - Paul J Keall
- ACRF Image X Institute, University of Sydney, Sydney, NSW, 2006, Australia
| | - Cornelis A T van den Berg
- Department of Radiotherapy, University Medical Centre Utrecht, PO box 85500, 3508 GA, Utrecht, The Netherlands
| | - Marco Riboldi
- Department of Medical Physics, Ludwig-Maximilians-Universität München, Am Coulombwall 1, 85748, Garching, Germany.
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Gurney-Champion OJ, Mahmood F, van Schie M, Julian R, George B, Philippens MEP, van der Heide UA, Thorwarth D, Redalen KR. Quantitative imaging for radiotherapy purposes. Radiother Oncol 2020; 146:66-75. [PMID: 32114268 PMCID: PMC7294225 DOI: 10.1016/j.radonc.2020.01.026] [Citation(s) in RCA: 59] [Impact Index Per Article: 14.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 01/22/2020] [Accepted: 01/29/2020] [Indexed: 02/07/2023]
Abstract
Quantitative imaging biomarkers show great potential for use in radiotherapy. Quantitative images based on microscopic tissue properties and tissue function can be used to improve contouring of the radiotherapy targets. Furthermore, quantitative imaging biomarkers might be used to predict treatment response for several treatment regimens and hence be used as a tool for treatment stratification, either to determine which treatment modality is most promising or to determine patient-specific radiation dose. Finally, patient-specific radiation doses can be further tailored to a tissue/voxel specific radiation dose when quantitative imaging is used for dose painting. In this review, published standards, guidelines and recommendations on quantitative imaging assessment using CT, PET and MRI are discussed. Furthermore, critical issues regarding the use of quantitative imaging for radiation oncology purposes and resultant pending research topics are identified.
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Affiliation(s)
- Oliver J Gurney-Champion
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom.
| | - Faisal Mahmood
- Department of Oncology, Odense University Hospital, Denmark; Department of Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Marcel van Schie
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Robert Julian
- Department of Radiotherapy Physics, Royal Surrey NHS Foundation Trust, Guildford, United Kingdom
| | - Ben George
- Radiation Therapy Medical Physics Group, CRUK/MRC Oxford Institute for Radiation Oncology, University of Oxford, United Kingdom
| | | | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, Eberhard Karls University of Tübingen, Germany
| | - Kathrine R Redalen
- Department of Physics, Norwegian University of Science and Technology, Trondheim, Norway
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27
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CT Texture Analysis Challenges: Influence of Acquisition and Reconstruction Parameters: A Comprehensive Review. Diagnostics (Basel) 2020; 10:diagnostics10050258. [PMID: 32353924 PMCID: PMC7277097 DOI: 10.3390/diagnostics10050258] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 04/22/2020] [Accepted: 04/24/2020] [Indexed: 12/15/2022] Open
Abstract
Texture analysis in medical imaging is a promising tool that is designed to improve the characterization of abnormal images from patients, to ultimately serve as a predictive or prognostic biomarker. However, the nature of image acquisition itself implies variability in each pixel/voxel value that could jeopardize the usefulness of texture analysis in the medical field. In this review, a search was performed to identify current published data for computed tomography (CT) texture reproducibility and variability. On the basis of this analysis, the critical steps were identified with a view of using texture analysis as a reliable tool in medical imaging. The need to specify the CT scanners used and the associated parameters in published studies is highlighted. Harmonizing acquisition parameters between studies is a crucial step for future texture analysis.
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28
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Wu J, Gensheimer MF, Zhang N, Guo M, Liang R, Zhang C, Fischbein N, Pollom EL, Beadle B, Le QT, Li R. Tumor Subregion Evolution-Based Imaging Features to Assess Early Response and Predict Prognosis in Oropharyngeal Cancer. J Nucl Med 2020; 61:327-336. [PMID: 31420498 PMCID: PMC7067523 DOI: 10.2967/jnumed.119.230037] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2019] [Accepted: 07/29/2019] [Indexed: 12/19/2022] Open
Abstract
The incidence of oropharyngeal squamous cell carcinoma (OPSCC) has been rapidly increasing. Disease stage and smoking history are often used in current clinical trials to select patients for deintensification therapy, but these features lack sufficient accuracy for predicting disease relapse. Our purpose was to develop an imaging signature to assess early response and predict outcomes of OPSCC. Methods: We retrospectively analyzed 162 OPSCC patients treated with concurrent chemoradiotherapy, equally divided into separate training and validation cohorts with similar clinical characteristics. A robust consensus clustering approach was used to spatially partition the primary tumor and involved lymph nodes into subregions (i.e., habitats) based on 18F-FDG PET and contrast CT imaging. We proposed quantitative image features to characterize the temporal volumetric change of the habitats and peritumoral/nodal tissue between baseline and midtreatment. The reproducibility of these features was evaluated. We developed an imaging signature to predict progression-free survival (PFS) by fitting an L1-regularized Cox regression model. Results: We identified 3 phenotypically distinct intratumoral habitats: metabolically active and heterogeneous, enhancing and heterogeneous, and metabolically inactive and homogeneous. The final Cox model consisted of 4 habitat evolution-based features. In both cohorts, this imaging signature significantly outperformed traditional imaging metrics, including midtreatment metabolic tumor volume for predicting PFS, with a C-index of 0.72 versus 0.67 (training) and 0.66 versus 0.56 (validation). The imaging signature stratified patients into high-risk versus low-risk groups with 2-y PFS rates of 59.1% versus 89.4% (hazard ratio, 4.4; 95% confidence interval, 1.4-13.4 [training]) and 61.4% versus 87.8% (hazard ratio, 4.6; 95% confidence interval, 1.7-12.1 [validation]). The imaging signature remained an independent predictor of PFS in multivariable analysis adjusting for stage, human papillomavirus status, and smoking history. Conclusion: The proposed imaging signature allows more accurate prediction of disease progression and, if prospectively validated, may refine OPSCC patient selection for risk-adaptive therapy.
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Affiliation(s)
- Jia Wu
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Michael F Gensheimer
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Nasha Zhang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Meiying Guo
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Rachel Liang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Carrie Zhang
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Nancy Fischbein
- Department of Radiology, Stanford University School of Medicine, Stanford, California
| | - Erqi L Pollom
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Beth Beadle
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Quynh-Thu Le
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
| | - Ruijiang Li
- Department of Radiation Oncology, Stanford University School of Medicine, Stanford, California; and
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29
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Mashayekhi R, Parekh VS, Faghih M, Singh VK, Jacobs MA, Zaheer A. Radiomic features of the pancreas on CT imaging accurately differentiate functional abdominal pain, recurrent acute pancreatitis, and chronic pancreatitis. Eur J Radiol 2019; 123:108778. [PMID: 31846864 DOI: 10.1016/j.ejrad.2019.108778] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2019] [Revised: 11/25/2019] [Accepted: 11/30/2019] [Indexed: 02/07/2023]
Abstract
PURPOSE Patients with recurrent abdominal pain and pancreatic enzyme elevations may be diagnosed clinically with recurrent acute pancreatitis (RAP) even with normal imaging or no imaging at all. Since neither abdominal pain nor enzyme elevations are specific for acute pancreatitis (AP), and patients with RAP often have a normal appearing pancreas on CT after resolution of an AP episode, RAP diagnosis can be challenging. This study aims to determine if quantitative radiomic features of the pancreas on CT can differentiate patients with functional abdominal pain, RAP, and chronic pancreatitis (CP). METHOD Contrast enhanced CT abdominal images of adult patients evaluated in a pancreatitis clinic from 2010 to 2018 with the diagnosis of RAP, functional abdominal pain, or CP were retrospectively reviewed. The pancreas was outlined by drawing region of interest (ROI) on images. 54 radiomic features were extracted from each ROI and were compared between the patient groups. A one-vs-one Isomap and Support Vector Machine (IsoSVM) classifier was also trained and tested to classify patients into one of the three diagnostic groups based on their radiomic features. RESULTS Among the study's 56 patients, 20 (35.7 %) had RAP, 19 (33.9 %) had functional abdominal pain, and 17 (30.4 %) had CP. On univariate analysis, 11 radiomic features (10 GLCM features and one NGTDM feature) were significantly different between the patient groups. The IsoSVM classifier for prediction of patient diagnosis had an overall accuracy of 82.1 %. CONCLUSIONS Certain radiomic features on CT imaging can differentiate patients with functional abdominal pain, RAP, and CP.
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Affiliation(s)
- Rouzbeh Mashayekhi
- The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Vishwa S Parekh
- The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA; Department of Computer Science, The Johns Hopkins University, Baltimore, MD, 21208, USA.
| | - Mahya Faghih
- Pancreatitis Center, Division of Gastroenterology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Vikesh K Singh
- Pancreatitis Center, Division of Gastroenterology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Michael A Jacobs
- The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
| | - Atif Zaheer
- The Russell H. Morgan Department of Radiology and Radiological Sciences, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA; Pancreatitis Center, Division of Gastroenterology, Department of Medicine, The Johns Hopkins University School of Medicine, Baltimore, MD, 21205, USA.
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30
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Clark CH, Gagliardi G, Heijmen B, Malicki J, Thorwarth D, Verellen D, Muren LP. Adapting training for medical physicists to match future trends in radiation oncology. Phys Imaging Radiat Oncol 2019; 11:71-75. [PMID: 33458282 PMCID: PMC7807663 DOI: 10.1016/j.phro.2019.09.003] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022] Open
Affiliation(s)
- Catharine H. Clark
- Medical Physics, St Lukes Cancer Centre, Royal Surrey County Hospital, Guildford, UK
- Dept Medical Physics, National Physical Laboratory, Teddington, UK
| | - Giovanna Gagliardi
- Department of Medical Radiation Physics and Nuclear Medicine, Karolinska University Hospital, Sweden
- Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden
| | - Ben Heijmen
- Department of Radiation Oncology, Erasmus MC Cancer Institute, Rotterdam, The Netherlands
| | - Julian Malicki
- Department of Electroradiology, Poznań University of Medical Sciences, Poznań, Poland
- Department of Medical Physics, Greater Poland Cancer Centre, Poznań, Poland
| | - Daniela Thorwarth
- Section for Biomedical Physics, Department of Radiation Oncology, University of Tübingen, Germany
| | - Dirk Verellen
- Iridium Kankernetwerk, Antwerp, Belgium; University of Antwerp, Faculty of Medicine and Health Sciences, Belgium
| | - Ludvig P. Muren
- Department of Medical Physics, Aarhus University Hospital/Aarhus University, Denmark
- Danish Centre for Particle Therapy, Aarhus University Hospital, Denmark
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