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

• Published in: Scientific data 2017-09, Vol.4 (1), p.170117-170117, Article 170117
• Main Authors: Bakas, Spyridon, Akbari, Hamed, Sotiras, Aristeidis, Bilello, Michel, Rozycki, Martin, Kirby, Justin S., Freymann, John B., Farahani, Keyvan, Davatzikos, Christos
• Format: Article
• Language: English
• Subjects: 631/114/1564; 631/114/2397; 631/67/2321; 692/308/575; 692/699/67/1922; Brain Neoplasms - diagnostic imaging; Brain Neoplasms - genetics; Brain tumors; Cancer; Central nervous system; Data Descriptor; DNA, Neoplasm; Genomes; Glioblastoma; Glioma; Glioma - diagnostic imaging; Glioma - genetics; Humanities and Social Sciences; Humans; Image Interpretation, Computer-Assisted; Image processing; Magnetic Resonance Imaging; multidisciplinary; Multimodal Imaging; NMR; Nuclear magnetic resonance; Radiomics; Science; Segmentation
• ISSN: 2052-4463; 2052-4463
• DOI: 10.1038/sdata.2017.117

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. Design Type(s) parallel group design • data integration objective Measurement Type(s) nuclear magnetic resonance assay Technology Type(s) MRI Scanner Factor Type(s) diagnosis Sample Characteristic(s) Homo sapiens • glioma cell Machine-accessible metadata file describing the reported data (ISA-Tab format)