PofoliaShared via Pofolia

Scientific Data· 2017Q1

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

Spyridon Bakas, Hamed Akbari, Aristeidis Sotiras, Michel Bilello et al.

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 ↗

TakeawaysPremium
Ask the paperFree account

Continue with a free account

Ask the paper: 3 free questions a day about this paper; save it, get its citation, new summaries every day for your field. Takeaways are Premium.

Continue free on the web

Sign in with Google or Apple; no card needed. You come back to this paper.

On your phone:

Field: Radiology, Nuclear Medicine and Imaging

Radiology, Nuclear Medicine and ImagingMedicine