Literature DB >> 28872634

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

Spyridon Bakas1,2, Hamed Akbari1,2, Aristeidis Sotiras1,2, Michel Bilello1,2, Martin Rozycki1,2, Justin S Kirby3, John B Freymann3, Keyvan Farahani4, Christos Davatzikos1,2.   

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.

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Year:  2017        PMID: 28872634      PMCID: PMC5685212          DOI: 10.1038/sdata.2017.117

Source DB:  PubMed          Journal:  Sci Data        ISSN: 2052-4463            Impact factor:   6.444


  61 in total

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Review 2.  Advances in functional and structural MR image analysis and implementation as FSL.

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Journal:  Neuroimage       Date:  2004       Impact factor: 6.556

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Journal:  Curr Neurol Neurosci Rep       Date:  2015-01       Impact factor: 5.081

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Journal:  IEEE Trans Med Imaging       Date:  2010-04-08       Impact factor: 10.048

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Authors:  Maciej A Mazurowski
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Authors:  David A Gutman; Lee A D Cooper; Scott N Hwang; Chad A Holder; Jingjing Gao; Tarun D Aurora; William D Dunn; Lisa Scarpace; Tom Mikkelsen; Rajan Jain; Max Wintermark; Manal Jilwan; Prashant Raghavan; Erich Huang; Robert J Clifford; Pattanasak Mongkolwat; Vladimir Kleper; John Freymann; Justin Kirby; Pascal O Zinn; Carlos S Moreno; Carl Jaffe; Rivka Colen; Daniel L Rubin; Joel Saltz; Adam Flanders; Daniel J Brat
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Review 9.  FSL.

Authors:  Mark Jenkinson; Christian F Beckmann; Timothy E J Behrens; Mark W Woolrich; Stephen M Smith
Journal:  Neuroimage       Date:  2011-09-16       Impact factor: 6.556

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  200 in total

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2.  Multi-Institutional Deep Learning Modeling Without Sharing Patient Data: A Feasibility Study on Brain Tumor Segmentation.

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4.  A Roadmap for Foundational Research on Artificial Intelligence in Medical Imaging: From the 2018 NIH/RSNA/ACR/The Academy Workshop.

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5.  Technical Note: MRQy - An open-source tool for quality control of MR imaging data.

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6.  Analyzing magnetic resonance imaging data from glioma patients using deep learning.

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Journal:  Comput Med Imaging Graph       Date:  2020-12-02       Impact factor: 4.790

7.  Epidermal Growth Factor Receptor Extracellular Domain Mutations in Glioblastoma Present Opportunities for Clinical Imaging and Therapeutic Development.

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8.  Radiomic profiles in diffuse glioma reveal distinct subtypes with prognostic value.

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9.  Confidence Calibration and Predictive Uncertainty Estimation for Deep Medical Image Segmentation.

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10.  Towards Population-Based Histologic Stain Normalization of Glioblastoma.

Authors:  Caleb M Grenko; Angela N Viaene; MacLean P Nasrallah; Michael D Feldman; Hamed Akbari; Spyridon Bakas
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