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Lost J, Verma T, Jekel L, von Reppert M, Tillmanns N, Merkaj S, Petersen GC, Bahar R, Gordem A, Haider MA, Subramanian H, Brim W, Ikuta I, Omuro A, Conte GM, Marquez-Nostra BV, Avesta A, Bousabarah K, Nabavizadeh A, Kazerooni AF, Aneja S, Bakas S, Lin M, Sabel M, Aboian M. Systematic Literature Review of Machine Learning Algorithms Using Pretherapy Radiologic Imaging for Glioma Molecular Subtype Prediction. AJNR Am J Neuroradiol 2023; 44:1126-1134. [PMID: 37770204 PMCID: PMC10549943 DOI: 10.3174/ajnr.a8000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Accepted: 08/01/2023] [Indexed: 10/03/2023]
Abstract
BACKGROUND The molecular profile of gliomas is a prognostic indicator for survival, driving clinical decision-making for treatment. Pathology-based molecular diagnosis is challenging because of the invasiveness of the procedure, exclusion from neoadjuvant therapy options, and the heterogeneous nature of the tumor. PURPOSE We performed a systematic review of algorithms that predict molecular subtypes of gliomas from MR Imaging. DATA SOURCES Data sources were Ovid Embase, Ovid MEDLINE, Cochrane Central Register of Controlled Trials, Web of Science. STUDY SELECTION Per the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 12,318 abstracts were screened and 1323 underwent full-text review, with 85 articles meeting the inclusion criteria. DATA ANALYSIS We compared prediction results from different machine learning approaches for predicting molecular subtypes of gliomas. Bias analysis was conducted for each study, following the Prediction model Risk Of Bias Assessment Tool (PROBAST) guidelines. DATA SYNTHESIS Isocitrate dehydrogenase mutation status was reported with an area under the curve and accuracy of 0.88 and 85% in internal validation and 0.86 and 87% in limited external validation data sets, respectively. For the prediction of O6-methylguanine-DNA methyltransferase promoter methylation, the area under the curve and accuracy in internal validation data sets were 0.79 and 77%, and in limited external validation, 0.89 and 83%, respectively. PROBAST scoring demonstrated high bias in all articles. LIMITATIONS The low number of external validation and studies with incomplete data resulted in unequal data analysis. Comparing the best prediction pipelines of each study may introduce bias. CONCLUSIONS While the high area under the curve and accuracy for the prediction of molecular subtypes of gliomas are reported in internal and external validation data sets, limited use of external validation and the increased risk of bias in all articles may present obstacles for clinical translation of these techniques.
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Affiliation(s)
- Jan Lost
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
- Department of Neurosurgery (J.L., M.S.), Heinrich-Heine-University, Duesseldorf, Germany
| | - Tej Verma
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Leon Jekel
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Marc von Reppert
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Niklas Tillmanns
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Sara Merkaj
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Gabriel Cassinelli Petersen
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ryan Bahar
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ayyüce Gordem
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Muhammad A Haider
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Harry Subramanian
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Waverly Brim
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Ichiro Ikuta
- Department of Radiology (I.I.), Mayo Clinic Arizona, Phoenix, Arizona
| | - Antonio Omuro
- Department of Neurology and Yale Cancer Center (A.O.), Yale School of Medicine, New Haven, Connecticut
| | - Gian Marco Conte
- Department of Radiology (G.M.C.), Mayo Clinic, Rochester, Minesotta
| | - Bernadette V Marquez-Nostra
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | - Arman Avesta
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
| | | | - Ali Nabavizadeh
- Department of Radiology (A.N.), Perelman School of Medicine, Hospital of University of Pennsylvania, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Anahita Fathi Kazerooni
- Department of Neurosurgery (A.F.K.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Division of Neurosurgery (A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
- Center for Data-Driven Discovery (A.F.K.), Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Sanjay Aneja
- Department of Therapeutic Radiology (S.A), Yale School of Medicine, New Haven, Connecticut
| | - Spyridon Bakas
- Center for Biomedical Image Computing and Analytics (S.B.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Richards Medical Research Laboratories (S.B.), Philadelphia, Pennsylvania
- Department of Radiology (S.B.), Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - MingDe Lin
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
- Visage Imaging Inc (K.B., M.L.), San Diego, California
| | - Michael Sabel
- Department of Neurosurgery (J.L., M.S.), Heinrich-Heine-University, Duesseldorf, Germany
| | - Mariam Aboian
- From the Department of Radiology and Biomedical Imaging (J.L., T.V., L.J., M.v.R., N.T., S.M., G.C.P., R.B., A.G., M.A.H., H.S., W.B., B.V.M.-N., A.A., M.L., M.A.), Yale School of Medicine, New Haven, Connecticut
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Ramesh KK, Xu KM, Trivedi AG, Huang V, Sharghi VK, Kleinberg LR, Mellon EA, Shu HKG, Shim H, Weinberg BD. A Fully Automated Post-Surgical Brain Tumor Segmentation Model for Radiation Treatment Planning and Longitudinal Tracking. Cancers (Basel) 2023; 15:3956. [PMID: 37568773 PMCID: PMC10417353 DOI: 10.3390/cancers15153956] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/26/2023] [Accepted: 08/02/2023] [Indexed: 08/13/2023] Open
Abstract
Glioblastoma (GBM) has a poor survival rate even with aggressive surgery, concomitant radiation therapy (RT), and adjuvant chemotherapy. Standard-of-care RT involves irradiating a lower dose to the hyperintense lesion in T2-weighted fluid-attenuated inversion recovery MRI (T2w/FLAIR) and a higher dose to the enhancing tumor on contrast-enhanced, T1-weighted MRI (CE-T1w). While there have been several attempts to segment pre-surgical brain tumors, there have been minimal efforts to segment post-surgical tumors, which are complicated by a resection cavity and postoperative blood products, and tools are needed to assist physicians in generating treatment contours and assessing treated patients on follow up. This report is one of the first to train and test multiple deep learning models for the purpose of post-surgical brain tumor segmentation for RT planning and longitudinal tracking. Post-surgical FLAIR and CE-T1w MRIs, as well as their corresponding RT targets (GTV1 and GTV2, respectively) from 225 GBM patients treated with standard RT were trained on multiple deep learning models including: Unet, ResUnet, Swin-Unet, 3D Unet, and Swin-UNETR. These models were tested on an independent dataset of 30 GBM patients with the Dice metric used to evaluate segmentation accuracy. Finally, the best-performing segmentation model was integrated into our longitudinal tracking web application to assign automated structured reporting scores using change in percent cutoffs of lesion volume. The 3D Unet was our best-performing model with mean Dice scores of 0.72 for GTV1 and 0.73 for GTV2 with a standard deviation of 0.17 for both in the test dataset. We have successfully developed a lightweight post-surgical segmentation model for RT planning and longitudinal tracking.
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Affiliation(s)
- Karthik K. Ramesh
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Karen M. Xu
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
| | - Anuradha G. Trivedi
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | - Vicki Huang
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
| | | | - Lawrence R. Kleinberg
- Department of Radiation Oncology, Johns Hopkins University, Baltimore, MD 21218, USA
| | - Eric A. Mellon
- Department of Radiation Oncology, Sylvester Comprehensive Cancer Center, Miller School of Medicine, University of Miami, Miami, FL 45056, USA
| | - Hui-Kuo G. Shu
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Hyunsuk Shim
- Department of Radiation Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA; (K.K.R.); (A.G.T.); (V.H.)
- Department of Biomedical Engineering, Emory University and Georgia Institute of Technology, Atlanta, GA 30332, USA
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Brent D. Weinberg
- Winship Cancer Institute, Emory University School of Medicine, Atlanta, GA 30322, USA
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
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Aboian M, Bousabarah K, Kazarian E, Zeevi T, Holler W, Merkaj S, Cassinelli Petersen G, Bahar R, Subramanian H, Sunku P, Schrickel E, Bhawnani J, Zawalich M, Mahajan A, Malhotra A, Payabvash S, Tocino I, Lin M, Westerhoff M. Clinical implementation of artificial intelligence in neuroradiology with development of a novel workflow-efficient picture archiving and communication system-based automated brain tumor segmentation and radiomic feature extraction. Front Neurosci 2022; 16:860208. [PMID: 36312024 PMCID: PMC9606757 DOI: 10.3389/fnins.2022.860208] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2022] [Accepted: 07/13/2022] [Indexed: 11/18/2022] Open
Abstract
Purpose Personalized interpretation of medical images is critical for optimum patient care, but current tools available to physicians to perform quantitative analysis of patient’s medical images in real time are significantly limited. In this work, we describe a novel platform within PACS for volumetric analysis of images and thus development of large expert annotated datasets in parallel with radiologist performing the reading that are critically needed for development of clinically meaningful AI algorithms. Specifically, we implemented a deep learning-based algorithm for automated brain tumor segmentation and radiomics extraction, and embedded it into PACS to accelerate a supervised, end-to- end workflow for image annotation and radiomic feature extraction. Materials and methods An algorithm was trained to segment whole primary brain tumors on FLAIR images from multi-institutional glioma BraTS 2021 dataset. Algorithm was validated using internal dataset from Yale New Haven Health (YHHH) and compared (by Dice similarity coefficient [DSC]) to radiologist manual segmentation. A UNETR deep-learning was embedded into Visage 7 (Visage Imaging, Inc., San Diego, CA, United States) diagnostic workstation. The automatically segmented brain tumor was pliable for manual modification. PyRadiomics (Harvard Medical School, Boston, MA) was natively embedded into Visage 7 for feature extraction from the brain tumor segmentations. Results UNETR brain tumor segmentation took on average 4 s and the median DSC was 86%, which is similar to published literature but lower than the RSNA ASNR MICCAI BRATS challenge 2021. Finally, extraction of 106 radiomic features within PACS took on average 5.8 ± 0.01 s. The extracted radiomic features did not vary over time of extraction or whether they were extracted within PACS or outside of PACS. The ability to perform segmentation and feature extraction before radiologist opens the study was made available in the workflow. Opening the study in PACS, allows the radiologists to verify the segmentation and thus annotate the study. Conclusion Integration of image processing algorithms for tumor auto-segmentation and feature extraction into PACS allows curation of large datasets of annotated medical images and can accelerate translation of research into development of personalized medicine applications in the clinic. The ability to use familiar clinical tools to revise the AI segmentations and natively embedding the segmentation and radiomic feature extraction tools on the diagnostic workstation accelerates the process to generate ground-truth data.
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Affiliation(s)
- Mariam Aboian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
- *Correspondence: Mariam Aboian,
| | | | - Eve Kazarian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Tal Zeevi
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | | | - Sara Merkaj
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Gabriel Cassinelli Petersen
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Ryan Bahar
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Harry Subramanian
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Pranay Sunku
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Elizabeth Schrickel
- Department of Radiology and Biomedical Imaging, Brain Tumor Research Group, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Jitendra Bhawnani
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Mathew Zawalich
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Amit Mahajan
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Ajay Malhotra
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Sam Payabvash
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - Irena Tocino
- Department of Radiology and Biomedical Imaging, Yale School of Medicine, Yale University, New Haven, CT, United States
| | - MingDe Lin
- Department of Radiology, Yale University and Visage Imaging, New Haven, CT, United States
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