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  • 标题:Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
  • 本地全文:下载
  • 作者:Spyridon Bakas ; Hamed Akbari ; Aristeidis Sotiras
  • 期刊名称:Scientific Data
  • 电子版ISSN:2052-4463
  • 出版年度:2017
  • 卷号:4
  • DOI:10.1038/sdata.2017.117
  • 语种:English
  • 出版社:Nature Publishing Group
  • 摘要: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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