Scientific Data· 2017Q1
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
- 2,989citations
- Q1SCImago
- 2017year
Short summary
Researchers released expert-revised segmentation labels and radiomic features for 243 pre-operative multimodal MRI scans of The Cancer Genome Atlas (TCGA) glioma collections, enabling reproducible quantitative studies and computer-aided segmentation method evaluation.
AI-generated from the title and abstract; the full text is not read.
Key points
- Segmentation labels and radiomic features released for 243 TCGA glioma MRI scans.
- Labels were manually revised by an expert neuroradiologist.
- Data covers both glioblastoma (n=135) and low-grade-glioma (n=108) sub-regions.
- Enables reproducible quantitative studies and evaluation of segmentation methods.
AI-generated from the title and abstract; the full text is not read.
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.
The authors' abstract, as published at the source. Scientific Data, 2017 · DOI ↗
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Field: Radiology, Nuclear Medicine and Imaging
Radiology, Nuclear Medicine and ImagingMedicine