Radiology imaging is playing an increasingly vital role in the diagnosis of COVID-19 patients and determining therapeutic options, patient care management and new research directions. Publicly available imaging data is essential to drive new research by permitting the creation of large multi-site cohorts for machine learning based analyses. All too frequently rural populations are underrepresented in such public collections. In fact, the literature demonstrates there is very limited data on COVID-19 outcomes in rural populations, while it is well established that such populations have differentially high expression of key comorbidities. Similarly, while the number of genomes of the SARS-COV-2 virus are rapidly growing in public repositories, few samples represent the variants expressed in rural populations. This gap in available data is of particular importance given that the southern United States, as of July 2020, is the most rapidly expanding COVID-19 hot spot on earth. We have published a collection of radiographic and CT imaging studies for patients who tested positive for COVID-19. Each patient is described by a limited set of clinical data correlates that includes demographics, comorbidities, selected lab data and key radiology findings. These data are cross-linked to SARS-COV-2 cDNA sequence data extracted from clinical isolates from the same population, uploaded to the Genbank repository. We believe this collection will help to define appropriate correlative data and contribute samples from this normally underrepresented population to the global research community.
We would like to acknowledge the individuals and institutions that have provided data for this collection:
The University of Arkansas for Medical Sciences (UAMS) Translational Research Institute, Department of Radiology, Department of Biomedical Informatics and Department of Surgery, Little Rock, Arkansas, USA.
TCIA COVID-19 Datasets
Additional datasets and information about TCIA efforts to support COVID-19 research can be found here.
|Data Type||Download all or Query/Filter|
Images (DICOM, 19.0 GB)
(Download requires the NBIA Data Retriever)
|Clinical data (CSV)|
Viral Genomes Genbank repository (web)
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CT, CR, DX
Number of Patients
Number of Studies
Number of Series
Number of Images
|Images Size (GB)||19.0|
Citations & Data Usage Policy
Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 4.0 International License under which it has been published. Attribution should include references to the following citations:
Desai, S., Baghal, A., Wongsurawat, T., Al-Shukri, S., Gates, K., Farmer, P., Rutherford, M., Blake, G.D., Nolan, T., Powell, T., Sexton, K., Bennett, W., Prior, F. (2020). Data from Chest Imaging with Clinical and Genomic Correlates Representing a Rural COVID-19 Positive Population [Data set]. The Cancer Imaging Archive. DOI: https://doi.org/10.7937/tcia.2020.py71-5978.
Desai, S., Baghal, A., Wongsurawat, T., Jenjaroenpun, P., Powell, T., Al-Shukri, S., Gates, K., Farmer, P., Rutherford, M., Blake, G., Nolan, T., Sexton, K., Bennett, W., Smith, K., Syed, S., Prior, F. (2020). Chest imaging representing a COVID-19 positive rural U.S. population. Scientific Data. 2020;7(1):414. doi: https://doi.org/10.1038/s41597-020-00741-6.
Jenjaroenpun, P., Wanchai, V., OnoMoore, K.D., Laudadio, J., James, L.P., Adams, S.H., Prior, F., Nookaew, I., Ussery, D.W., Wongsurawat, T. (2020). Two SARS-CoV-2 genome sequences of isolates from rural U.S. patients harboring the D614G mutation, obtained using Nanopore sequencing. Microbiology Resource Announcements, 2020. DOI: 10.1128/MRA.01109-20.
This project has been funded in whole or in part with federal funds from the National Center for Advancing Translational Sciences UL1 TR003107 and the National Cancer Institute, Contract No. 75N91019D00024, Subcontract 20X023F.
Clark K, Vendt B, Smith K, Freymann J, Kirby J, Koppel P, Moore S, Phillips S, Maffitt D, Pringle M, Tarbox L, Prior F. The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, Journal of Digital Imaging, Volume 26, Number 6, December, 2013, pp 1045-1057. DOI: 10.1007/s10278-013-9622-7