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

TLDR

Gliomas are central nervous system tumors with various sub‑regions, and accurate labeling of these sub‑regions in imaging is essential for clinical and computational studies, including radiomic and radiogenomic analyses. We release segmentation labels and radiomic features for all pre‑operative multimodal MRI scans (n = 243) from the TCGA glioma collections, publicly available in TCIA. Pre‑operative scans from TCGA‑GBM (n = 135) and TCGA‑LGG (n = 108) were identified radiologically, segmented using an automated state‑of‑the‑art method and manually refined by a board‑certified neuroradiologist, and extensive radiomic features were extracted from the revised labels. The released labels and features enable reproducible quantitative studies, predictive, prognostic, and diagnostic assessments, and evaluation of computer‑aided segmentation methods against the state‑of‑the‑art baseline.

Abstract

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.