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Sci Data. 2017 Sep 5;4:170117. doi: 10.1038/sdata.2017.117.

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

Author information

1
Center 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.
2
Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories, Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USA.
3
Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research (FNLCR), Cancer Imaging Program (CIP), 8560 Progress Drive, Frederick, Maryland 21701, USA.
4
Cancer Imaging Program (CIP), National Cancer Institute (NCI), 9609 Medical Center Drive, Bethesda, Maryland 20892, USA.

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.

PMID:
28872634
PMCID:
PMC5685212
DOI:
10.1038/sdata.2017.117
[Indexed for MEDLINE]
Free PMC Article

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