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The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository

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Clark, Kenneth, Vendt, Bruce, Smith, Kirk, Freymann, John, Kirby, Justin, Koppel, Paul, Moore, Stephen, Phillips, Stanley, Maffitt, David, Pringle, Michael, Tarbox, Lawrence, Prior, Fred·2013·Journal of Digital Imaging

DOI 10.1007/s10278-013-9622-7PMC PMC3824915

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Abstract

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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Essen, D. C. V., Uǧurbil, K., Auerbach, E. J., Barch, D. M., Behrens, T. E. J., Bucholz, R. D., Chang, A., Chen, L., Corbetta, M., Curtiss, S. W., et al. (2012) · NeuroImage · doi:10.1016/j.neuroimage.2012.02.018 · Cited by 2,638
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GenBank
Benson, D. A., Boguski, M. S., Lipman, D. J., & Ostell, J. (1997) · Nucleic Acids Research · doi:10.1093/nar/25.1.1 · Cited by 171
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Keator, D. B., Grethe, J. S., Marcus, D., Özyurt, İ. B., Gadde, S., Murphy, S., Pieper, S. D., Greve, D. N., Notestine, R. J., Bockholt, H. J., et al. (2008) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2008.917893 · Cited by 123
Biomedical informatics research network: building a national collaboratory to hasten the derivation of new understanding and treatment of disease.
Grethe, J. S., Baru, C., Gupta, A., James, M., Ludaescher, B., Martone, M. E., Papadopoulos, P. M., Peltier, S. T., Rajasekar, A., Santini, S., et al. (2005) · PubMed · Cited by 80
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Birney, E., Bateman, A. G., Clamp, M. E., & Hubbard, T. (2001) · Nature · doi:10.1038/35057004 · Cited by 63
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Clarke, L. P., Croft, B. S., Nordstrom, R. J., Zhang, H., Kelloff, G. J., & Tatum, J. L. (2009) · Translational Oncology · doi:10.1593/tlo.09217 · Cited by 46
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Hampton, T. (2006) · JAMA · doi:10.1001/jama.296.16.1958-d · Cited by 45
Silent Cerebral Infarct Transfusion (SIT) Trial Imaging Core: Application of Novel Imaging Information Technology for Rapid and Central Review of MRI of the Brain
Vendt, B. A., McKinstry, R. C., Ball, W. S., Kraut, M. A., Prior, F., BARTON, B. A., Casella, J. F., & DeBaun, M. R. (2008) · Journal of Imaging Informatics in Medicine · doi:10.1007/s10278-008-9114-3 · Cited by 31
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Khalighi, S., Reddy, K. M., Midya, A., Pandav, K. B., Madabhushi, A., & Abedalthagafi, M. (2024) · npj Precision Oncology · doi:10.1038/s41698-024-00575-0 · Cited by 364
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Chen, S., Ma, K., & Zheng, Y. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1904.00625 · Cited by 656
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Koh, D., Papanikolaou, N. K., Bick, U., Illing, R. O., Kahn, C. E., Kalpathi-Cramer, J., Matos, C., Martí‐Bonmatí, L., Miles, A., Mun, S. K., et al. (2022) · Communications Medicine · doi:10.1038/s43856-022-00199-0 · Cited by 341
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Fang, R., Chen, X., Zhang, S., Shi, H., Ye, Y., Shi, H., Zou, Z., Li, P., Guo, Q., Ma, L., et al. (2021) · Nature Communications · doi:10.1038/s41467-020-20379-7 · Cited by 315
Spatial mapping of protein composition and tissue organization: a primer for multiplexed antibody-based imaging
Hickey, J. W., Neumann, E. K., Radtke, A. J., Camarillo, J. M., Beuschel, R. T., Albanese, A., McDonough, E., Hatler, J. M., Wiblin, A. E., Fisher, J., et al. (2021) · Nature Methods · doi:10.1038/s41592-021-01316-y · Cited by 314
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Bakr, S. H., Gevaert, O., Echegaray, S., Ayers, K., Zhou, M., Shafiq, M., Zheng, H., Benson, J. A., Zhang, W., Leung, A. N. C., et al. (2018) · Scientific Data · doi:10.1038/sdata.2018.202 · Cited by 319
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Lu, C., Hsu, F., Hsieh, K. L., Kao, Y. J., Cheng, S., Hsu, J. B., Tsai, P., Chen, R., Huang, C., Yen, Y., et al. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-3445 · Cited by 306
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Wu, P.-Y., Cheng, C., Kaddi, C. D., Venugopalan, J., Hoffman, R., & Wang, M. D. (2016) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2016.2573285 · Cited by 317
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Lee, H., Lee, J., Kim, H., Cho, B., & Cho, S. (2018) · IEEE Transactions on Radiation and Plasma Medical Sciences · doi:10.1109/trpms.2018.2867611 · Cited by 304
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Shakeel, P. M., Burhanuddin, M. A., & Desa, M. I. (2019) · Measurement · doi:10.1016/j.measurement.2019.05.027 · Cited by 302
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Ma, J., Wang, Y., An, X., Ge, C., Yu, Z., Chen, J., Zhu, Q., Dong, G., He, J., He, Z., et al. (2020) · Medical Physics · doi:10.1002/mp.14676 · Cited by 369
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Masood, A., Sheng, B., Li, P., Hou, X., Wei, X., Qin, J., & Feng, D. (2018) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2018.01.005 · Cited by 292
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Zhang, S., Xu, Y., Usuyama, N., Xu, H., Bagga, J., Tinn, R., Preston, S., Rao, R., Wei, M., Valluri, N., et al. (2025) · NEJM AI · doi:10.1056/aioa2400640 · Cited by 283
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Carré, A., Klausner, G., Edjlali, M., Lerousseau, M., Briend-Diop, J., Sun, R., Ammari, S., Reuzé, S., Andres, É. A., Estienne, T., et al. (2020) · Scientific Reports · doi:10.1038/s41598-020-69298-z · Cited by 297
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Lee, J., Wang, J., Sa, J. K., Ladewig, E., Lee, H., Lee, I., Kang, H. J., Rosenbloom, D. I. S., Cámara, P. G., Liu, Z., et al. (2017) · Nature Genetics · doi:10.1038/ng.3806 · Cited by 284
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Dong, X., Lei, Y., Wang, T., Thomas, M., Tang, L., Curran, W. J., Liu, T., & Yang, X. (2019) · Medical Physics · doi:10.1002/mp.13458 · Cited by 275
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Shen, S., Han, S. X., Aberle, D. R., Bui, A. A., & Hsu, W. (2019) · Expert Systems with Applications · doi:10.1016/j.eswa.2019.01.048 · Cited by 268
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Gatidis, S., Hepp, T., Früh, M., la Fougère, C. J., Nikolaou, K., Pfannenberg, C., Schölkopf, B., Küstner, T., Cyran, C. C., & Rubin, D. L. (2022) · Scientific Data · doi:10.1038/s41597-022-01718-3 · Cited by 263
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Zwanenburg, A., Leger, S., Agolli, L., Pilz, K., Troost, E. G. C., Richter, C., & Löck, S. (2019) · Scientific Reports · doi:10.1038/s41598-018-36938-4 · Cited by 258
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Chaunzwa, T. L., Hosny, A., Xu, Y., Shafer, A., Diao, N., Lanuti, M. D., Christiani, D. C., Mak, R. H., & Aerts, H. J. W. L. (2021) · Scientific Reports · doi:10.1038/s41598-021-84630-x · Cited by 269
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Tandel, G. S., Balestrieri, A., Jujaray, T., Khanna, N. N., Saba, L., & Suri, J. S. (2020) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2020.103804 · Cited by 266
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Systematic literature review: Quantum machine learning and its applications
García, D. P., Cruz-Benito, J., & García‐Peñalvo, F. J. (2024) · Computer Science Review · doi:10.1016/j.cosrev.2024.100619 · Cited by 372

Authors: Clark, K., Vendt, B., Smith, K., Freymann, J., Kirby, J., Koppel, P., Moore, S., Phillips, S., Maffitt, D., Pringle, M., Tarbox, L., & Prior, F.
Affiliations: Washington University in St. Louis (US)
Frederick National Laboratory for Cancer Research (US)
Corresponding author: Clark, Kenneth
ORCID: Freymann, John
Kirby, Justin
Moore, Stephen
Tarbox, Lawrence
Prior, Fred
Venue: Journal of Digital Imaging
Publisher: Springer Science and Business Media LLC
Published: 2013-07-25
Volume: 26
Issue: 6
Pages: 1045-1057
ISSN: 0897-1889 (ISSN-L)
1618-727X
DOI: 10.1007/s10278-013-9622-7
PMC: PMC3824915
OpenAlex: W2083927153
Cited by: 4,599
References: 15
Citations by year: 2026: 337 · 2025: 584 · 2024: 685 · 2023: 667 · 2022: 623 · 2021: 542 · 2020: 418 · 2019: 320 · 2018: 204 · 2017: 112 · 2016: 68 · 2015: 27 · 2014: 7 · 2013: 1
FWCI: 101.32
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: other-oa
www.springer.com/tdm (tdm)
Locations: Journal of Imaging Informatics in Medicine (publishedVersion, other-oa)
PubMed Central (publishedVersion)
PubMed (publishedVersion)
Funders: U.S. Department of Health and Human Services (HHSN261200800001E)
Radiological Society of North America
National Institutes of Health (Contract No. HHSN261200800001E., #HHSN261200800001E, HHSN261200800001C)
National Cancer Institute (No. HHSN261200800001E, Contract No.,HHSN261200800001E, HHSN261200800001E))
Topics: Physical Sciences › Computer Science › Information Systems › Research Data Management Practices
Social Sciences › Sociology and Political Science › Data Analysis and Archiving
Health Sciences › Health Professions › Health Information Management › Electronic Health Records Systems
Keywords: The Cancer Imaging Archive, medical image repositories, tumor imaging, open source software, data sharing, secondary research, repository management, user support
MeSH terms: Diagnostic Imaging, Female, Humans, Male, Medical Informatics, Neoplasms, Quality Control, Radiology Information Systems, Software, United States, Program Evaluation, Information Storage and Retrieval, National Cancer Institute (U.S.), Multimodal Imaging

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