Summary
As the COVID-19 pandemic unfolds, radiology imaging is playing an increasingly vital role in determining therapeutic options, patient care management and new research directions. Publicly available imaging data is essential to drive new research. All too frequently rural populations are underrepresented in such public collections. 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 unique set of clinical data correlates that includes demographics, comorbidities, selected lab data and key radiology findings. This data is cross-linked to SARS-COV-2 cDNA sequence data extracted from clinical isolates from the same population, published in GISAID. We believe this collection will help to define appropriate correlative data and contribute samples from this normally underrepresented population to the research community.Acknowledgements
We would like to acknowledge the individuals and institutions that have provided data for this collection:
TR003107 and the UAMS Translational Research Institute, Department of Radiology and Department of Biomedical Informatics - Special thanks to First Last Names, degree PhD, MD, etc from the Department of xxxxxx, Additional Names from same location.
- Continue with any names from additional submitting sites if collection consists of more that one.
Data Access
Data Type | Download all or Query/Filter |
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Images, Segmentations, and Radiation Therapy Structures/Doses/Plans (DICOM, XX.X GB) << latter two items only if DICOM SEG/RTSTRUCT/RTDOSE/PLAN exist >> | (Download requires the NBIA Data Retriever) |
Tissue Slide Images (SVS, XX.X GB) | |
Clinical data (CSV) | |
Genomics (web) |
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Detailed Description
Image Statistics | |
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Modalities | CT and X-Ray |
Number of Patients | 88 |
Number of Studies | 197 |
Number of Series | |
Number of Images | |
Images Size (GB) |
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Citations & Data Usage Policy
Users of this data must abide by the TCIA Data Usage Policy and the Creative Commons Attribution 3.0 Unported License under which it has been published. Attribution should include references to the following citations:
Data Citation
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Publication Citation
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Acknowledgement
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TCIA Citation
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
Other Publications Using This Data
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Version X (Current): Updated yyyy/mm/dd
Data Type | Download all or Query/Filter |
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Images (DICOM, xx.x GB) | (Requires NBIA Data Retriever.) |
Clinical Data (CSV) | Link |
Other (format) |
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