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

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 seg...

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Autores principales: Bakas, Spyridon, Akbari, Hamed, Sotiras, Aristeidis, Bilello, Michel, Rozycki, Martin, Kirby, Justin S., Freymann, John B., Farahani, Keyvan, Davatzikos, Christos
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685212/
https://www.ncbi.nlm.nih.gov/pubmed/28872634
http://dx.doi.org/10.1038/sdata.2017.117
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author Bakas, Spyridon
Akbari, Hamed
Sotiras, Aristeidis
Bilello, Michel
Rozycki, Martin
Kirby, Justin S.
Freymann, John B.
Farahani, Keyvan
Davatzikos, Christos
author_facet Bakas, Spyridon
Akbari, Hamed
Sotiras, Aristeidis
Bilello, Michel
Rozycki, Martin
Kirby, Justin S.
Freymann, John B.
Farahani, Keyvan
Davatzikos, Christos
author_sort Bakas, Spyridon
collection PubMed
description 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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spelling pubmed-56852122017-11-17 Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features Bakas, Spyridon Akbari, Hamed Sotiras, Aristeidis Bilello, Michel Rozycki, Martin Kirby, Justin S. Freymann, John B. Farahani, Keyvan Davatzikos, Christos Sci Data Data Descriptor 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. Nature Publishing Group 2017-09-05 /pmc/articles/PMC5685212/ /pubmed/28872634 http://dx.doi.org/10.1038/sdata.2017.117 Text en Copyright © 2017, The Author(s) http://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/ The Creative Commons Public Domain Dedication waiver http://creativecommons.org/publicdomain/zero/1.0/ applies to the metadata files made available in this article.
spellingShingle Data Descriptor
Bakas, Spyridon
Akbari, Hamed
Sotiras, Aristeidis
Bilello, Michel
Rozycki, Martin
Kirby, Justin S.
Freymann, John B.
Farahani, Keyvan
Davatzikos, Christos
Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
title Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
title_full Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
title_fullStr Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
title_full_unstemmed Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
title_short Advancing The Cancer Genome Atlas glioma MRI collections with expert segmentation labels and radiomic features
title_sort advancing the cancer genome atlas glioma mri collections with expert segmentation labels and radiomic features
topic Data Descriptor
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5685212/
https://www.ncbi.nlm.nih.gov/pubmed/28872634
http://dx.doi.org/10.1038/sdata.2017.117
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