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

Scientific data - Tập 4 Số 1
Spyridon Bakas1, Hamed Akbari1, Aristeidis Sotiras1, Michel Bilello1, Martin Rozycki1, Justin Kirby2, John Freymann2, Keyvan Farahani3, Christos Davatzikos4
1Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, 19104, Pennsylvania, USA
2Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research (FNLCR), Cancer Imaging Program (CIP), 8560 Progress Drive, Frederick, 21701, Maryland, USA
3Cancer Imaging Program (CIP), National Cancer Institute (NCI), 9609 Medical Center Drive, Bethesda, 20892, Maryland, USA
4Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, 19104, Pennsylvania, USA

Tóm tắt

Abstract

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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