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

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          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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          The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository.

          The National Institutes of Health have placed significant emphasis on sharing of research data to support secondary research. Investigators have been encouraged to publish their clinical and imaging data as part of fulfilling their grant obligations. Realizing it was not sufficient to merely ask investigators to publish their collection of imaging and clinical data, the National Cancer Institute (NCI) created the open source National Biomedical Image Archive software package as a mechanism for centralized hosting of cancer related imaging. NCI has contracted with Washington University in Saint Louis to create The Cancer Imaging Archive (TCIA)-an open-source, open-access information resource to support research, development, and educational initiatives utilizing advanced medical imaging of cancer. In its first year of operation, TCIA accumulated 23 collections (3.3 million images). Operating and maintaining a high-availability image archive is a complex challenge involving varied archive-specific resources and driven by the needs of both image submitters and image consumers. Quality archives of any type (traditional library, PubMed, refereed journals) require management and customer service. This paper describes the management tasks and user support model for TCIA.
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            The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS).

            In this paper we report the set-up and results of the Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) organized in conjunction with the MICCAI 2012 and 2013 conferences. Twenty state-of-the-art tumor segmentation algorithms were applied to a set of 65 multi-contrast MR scans of low- and high-grade glioma patients-manually annotated by up to four raters-and to 65 comparable scans generated using tumor image simulation software. Quantitative evaluations revealed considerable disagreement between the human raters in segmenting various tumor sub-regions (Dice scores in the range 74%-85%), illustrating the difficulty of this task. We found that different algorithms worked best for different sub-regions (reaching performance comparable to human inter-rater variability), but that no single algorithm ranked in the top for all sub-regions simultaneously. Fusing several good algorithms using a hierarchical majority vote yielded segmentations that consistently ranked above all individual algorithms, indicating remaining opportunities for further methodological improvements. The BRATS image data and manual annotations continue to be publicly available through an online evaluation system as an ongoing benchmarking resource.
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              Quantizing for minimum distortion

              J. Max (1960)
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                Author and article information

                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                2052-4463
                05 September 2017
                2017
                : 4
                : 170117
                Affiliations
                [1 ]Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories , Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USA
                [2 ]Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Richards Medical Research Laboratories , Floor 7, 3700 Hamilton Walk, Philadelphia, Pennsylvania 19104, USA
                [3 ]Leidos Biomedical Research, Inc., Frederick National Laboratory for Cancer Research (FNLCR), Cancer Imaging Program (CIP) , 8560 Progress Drive, Frederick, Maryland 21701, USA
                [4 ]Cancer Imaging Program (CIP), National Cancer Institute (NCI) , 9609 Medical Center Drive, Bethesda, Maryland 20892, USA
                Author notes
                [a ] S.B. (email: s.bakas@ 123456uphs.upenn.edu )
                []

                S.B. conceived the study, provided initializations for the automated segmentation method, corrected segmentation labels, extracted radiomic features and wrote the manuscript. H.A. provided initializations for the automated segmentation method, corrected segmentation labels, extracted radiomic features and revised the manuscript. A.S. provided initializations for the automated segmentation method, corrected segmentation labels, and revised the manuscript. M.B. evaluated iteratively with S.B., H.A., and A.S. all the manually-revised segmentation labels until confirmation of their correctness, and revised the manuscript. M.R. pre-processed all images, created the metadata tables, quality checked the final provided data and revised the manuscript. J.K. assisted with the collection of metadata, the requirements for sharing the data in TCIA, and revised the manuscript. J.F. assisted with the collection of metadata, the requirements for sharing the data in TCIA, and revised the manuscript. K.F. assisted with the collection of metadata, sharing the data in TCIA, benchmarking of the segmentation algorithm, and revised the manuscript. C.D. supervised the study and revised the manuscript.

                Author information
                http://orcid.org/0000-0001-8734-6482
                http://orcid.org/0000-0001-9786-3707
                Article
                sdata2017117
                10.1038/sdata.2017.117
                5685212
                28872634
                7e81cd14-90d3-4ced-b328-f99ca6b5b385
                Copyright © 2017, The Author(s)

                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.

                History
                : 20 March 2017
                : 14 July 2017
                Categories
                Data Descriptor

                cns cancer,cancer imaging,image processing,translational research,computational models

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