The Cancer Imaging Archive (TCIA) Public Access

Space shortcuts

Child pages
  • Chest Imaging with Clinical and Genomic Correlates Representing a Rural COVID-19 Positive Population (COVID-19-AR)

Versions Compared

Old Version 8

changes.mady.by.user Fred Prior

Saved on

compared with

New Version Current

changes.mady.by.user Jeff Tobler

Saved on

Key

  • This line was added.
  • This line was removed.
  • Formatting was changed.

Summary

Excerpt
Image Added

Redirect
delay5
locationhttps://www.cancerimagingarchive.net/collection/covid-19-ar/
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 co-morbiditiescomorbidities.  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.

TCIA COVID-19 Datasets

Additional datasets and information about TCIA efforts to support COVID-19 research can be found here.

Acknowledgements

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 and , Department of Biomedical Informatics and Department of Surgery, Little Rock, Arkansas, USA.

...

Localtab Group


Localtab
activetrue
titleData Access

Data Access

Data TypeDownload all or Query/FilterLicense

Images (DICOM,

XX

19.

X

0 GB)

CTs, DX, 

Image RemovedImage Removed


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/70226443/COVID-19-AR-July132020_NBIA-manifest.tcia?version=1&modificationDate=1594658316635&api=v2



Tcia button generator
labelSearch
urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=COVID-19-AR



(Download requires 

the 

the NBIA Data Retriever)

Tcia cc by 4

Clinical data (CSV, 46 kB)
Image Removed


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/70226443/COVID-19%20AR%20Clinical%20Correlates%20July202020.xlsx?version=1&modificationDate=1594659431356&api=v2



Tcia cc by 4

Click the Versions tab for more info about data releases.

Please contact help@cancerimagingarchive.net  with any questions regarding usage.

Additional Resources for this Dataset

The following external resources have been made available by the data submitters.  These are not hosted or supported by TCIA, but may be useful to researchers utilizing this collection.

The NCI Cancer Research Data Commons (CRDC) provides access to additional data and a cloud-based data science infrastructure that connects data sets with analytics tools to allow users to share, integrate, analyze, and visualize cancer research data.



Localtab
titleDetailed Description

Detailed Description

Image Statistics


Modalities

CT and X-Ray, CR, DX

Number of Patients

105

Number of Studies

256

Number of Series

461

Number of Images

31,935

Images Size (GB)
<< Add any additional information as needed below. Likely would be something from site. >>
19.0



Localtab
titleCitations & Data Usage Policy

Citations & Data Usage Policy

public

tcia-
collection
limited-license-policy

Info
titleData Citation

DOI goes here. Create using Datacite with information from Collection Approval form

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.


Info
titleAcknowledgement

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. 


Info
titlePublication Citation

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

Info
titlePublication Citation

We ask on the proposal form if they have ONE traditional publication they'd like users to cite.


Info
titleAcknowledgementPublication Citation

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: https://doi.org/10.1128/MRA.01109-20. Only if they ask for special acknowledgments like funding sources, grant numbers, etc in their proposal.


Info
titleTCIA 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.  (2013). The Cancer Imaging Archive (TCIA): Maintaining and Operating a Public Information Repository, . In Journal of Digital Imaging , Volume (Vol. 26, Number Issue 6, December, 2013, pp 1045-1057. DOI: pp. 1045–1057). Springer Science and Business Media LLC. https://doi.org/10.1007/s10278-013-9622-7 PMCID: PMC3824915

Other Publications Using This Data

TCIA maintains a list of publications which leverage TCIA our data. If you have a manuscript you'd like to add please contact the TCIA's Helpdesk.


Localtab
titleVersions

Version

X

1 (Current): Updated

yyyy

2020/

mm

07/

dd

13

Data TypeDownload all or Query/Filter

Images (DICOM,

xx

19.

x GB)

0 GB)


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/70226443/COVID-19-AR-July132020_NBIA-manifest.tcia?version=1&modificationDate=1594658316635&api=v2



Tcia button generator
labelSearch
urlhttps://nbia.cancerimagingarchive.net/nbia-search/?MinNumberOfStudiesCriteria=1&CollectionCriteria=COVID-19-AR
Image RemovedImage Removed



(Requires NBIA Data Retriever.)

Clinical Data (CSV)

Image Removed

<< One or two sentences about what you changed since last version.  No note required for version 1. >> 


Tcia button generator
urlhttps://wiki.cancerimagingarchive.net/download/attachments/70226443/COVID-19%20AR%20Clinical%20Correlates%20July202020.xlsx?version=1&modificationDate=1594659431356&api=v2



Additional Resources for this Dataset