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Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features

Spyridon BakasCenter for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USAHamed AkbariCenter for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USAAristeidis SotirasCenter for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USAMichel BilelloCenter for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USAMartin RozyckiCenter for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USAJustin KirbyLeidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research (FNLCR), Cancer Imaging Program (CIP), 8560 Progress Drive, Frederick, Maryland 21701, USAJohn FreymannLeidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research (FNLCR), Cancer Imaging Program (CIP), 8560 Progress Drive, Frederick, Maryland 21701, USAKeyvan FarahaniCancer Imaging Program (CIP), National Cancer Institute (NCI), 9609 Medical Center Drive, Bethesda, Maryland 20892, USAChristos DavatzikosCenter for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USA
2017en
ABI

Аннотация

Gliomas belong to a group of central nervous system tumors, and consist of various sub-regions. Gold standard labeling of these sub-regions in radiographic imaging is essential for both clinical and computational studies, including radiomic and radiogenomic analyses. Towards this end, we release segmentation labels and radiomic features for all pre-operative multimodal magnetic resonance imaging (MRI) (n=243) of the multi-institutional glioma collections of The Cancer Genome Atlas (TCGA), publicly available in The Cancer Imaging Archive (TCIA). Pre-operative scans were identified in both glioblastoma (TCGA-GBM, n=135) and low-grade-glioma (TCGA-LGG, n=108) collections via radiological assessment. The glioma sub-region labels were produced by an automated state-of-the-art method and manually revised by an expert board-certified neuroradiologist. An extensive panel of radiomic features was extracted based on the manually-revised labels. This set of labels and features should enable i) direct utilization of the TCGA/TCIA glioma collections towards repeatable, reproducible and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments, as well as ii) performance evaluation of computer-aided segmentation methods, and comparison to our state-of-the-art method.

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