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      The public cancer radiology imaging collections of The Cancer Imaging Archive

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          Abstract

          The Cancer Imaging Archive (TCIA) is the U.S. National Cancer Institute’s repository for cancer imaging and related information. TCIA contains 30.9 million radiology images representing data collected from approximately 37,568 subjects. This data is organized into collections by tumor-type with many collections also including analytic results or clinical data. TCIA staff carefully de-identify and curate all incoming collections prior to making the information available via web browser or programmatic interfaces. Each published collection within TCIA is assigned a Digital Object Identifier that references the collection. Additionally, researchers who use TCIA data may publish the subset of information used in their analysis by requesting a TCIA generated Digital Object Identifier. This data descriptor is a review of a selected subset of existing publicly available TCIA collections. It outlines the curation and publication methods employed by TCIA and makes available 15 collections of cancer imaging data.

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          Most cited references19

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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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            Big data: The future of biocuration.

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              Evaluating variability in tumor measurements from same-day repeat CT scans of patients with non-small cell lung cancer.

              To evaluate the variability of tumor unidimensional, bidimensional, and volumetric measurements on same-day repeat computed tomographic (CT) scans in patients with non-small cell lung cancer. This HIPAA-compliant study was approved by the institutional review board, with informed patient consent. Thirty-two patients with non-small cell lung cancer, each of whom underwent two CT scans of the chest within 15 minutes by using the same imaging protocol, were included in this study. Three radiologists independently measured the two greatest diameters of each lesion on both scans and, during another session, measured the same tumors on the first scan. In a separate analysis, computer software was applied to assist in the calculation of the two greatest diameters and the volume of each lesion on both scans. Concordance correlation coefficients (CCCs) and Bland-Altman plots were used to assess the agreements between the measurements of the two repeat scans (reproducibility) and between the two repeat readings of the same scan (repeatability). The reproducibility and repeatability of the three radiologists' measurements were high (all CCCs, >or=0.96). The reproducibility of the computer-aided measurements was even higher (all CCCs, 1.00). The 95% limits of agreements for the computer-aided unidimensional, bidimensional, and volumetric measurements on two repeat scans were (-7.3%, 6.2%), (-17.6%, 19.8%), and (-12.1%, 13.4%), respectively. Chest CT scans are well reproducible. Changes in unidimensional lesion size of 8% or greater exceed the measurement variability of the computer method and can be considered significant when estimating the outcome of therapy in a patient. (c) RSNA, 2009.
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                Author and article information

                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group
                2052-4463
                19 September 2017
                2017
                : 4
                : 170124
                Affiliations
                [1 ]University of Arkansas for Medical Sciences , Little Rock, Arkansas 72205, USA
                [2 ]Emory University , Atlanta, Georgia 30322, USA
                [3 ]Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research , Frederick, Maryland 20892, USA
                [4 ]Washington University School of Medicine , St Louis, Missouri 63110, USA
                Author notes
                []

                F.P. is the PI of the Cancer Imaging Archive project and lead contributor to this manuscript. K.S. created the shared lists that support access to the referenced collections and contributed to the manuscript. A.S. created the DOIs for the referenced collections and contributed to the manuscript. J.K., K.C., T.N., W.B., and L.T. are key contributors to the ongoing operation of TCIA and contributed to the manuscript. J.F. is the project manager for the National Cancer Institute, authored many of the collection descriptions and contributed to the manuscript.

                Author information
                http://orcid.org/0000-0002-6314-5683
                Article
                sdata2017124
                10.1038/sdata.2017.124
                5827108
                28925987
                9ef90684-5520-46a0-aad5-600541b01768
                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
                : 22 October 2015
                : 01 August 2017
                Categories
                Data Descriptor

                radiography,cancer imaging,diagnostic markers,databases
                radiography, cancer imaging, diagnostic markers, databases

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