Prediction of obstructive coronary artery disease using coronary calcification and epicardial adipose tissue assessments from CT calcium scoring scans
We developed a novel machine learning model to predict obstructive CAD from non-contrast CTCS scans. Our findings highlight the potential clinical benefits of CTCS imaging in identifying patients likely to benefit from advanced imaging.
Authors
- Sanjay Rajagopalan (ORCID: https://orcid.org/0000-0001-6669-8163)
- Michelle C. Williams (ORCID: https://orcid.org/0000-0003-3556-2428)
- Ammar Hoori (ORCID: https://orcid.org/0000-0002-8458-1214)
- David L. Wilson (ORCID: https://orcid.org/0000-0001-9763-1463)
- David E. Newby (ORCID: https://orcid.org/0000-0001-7971-4628)
- Justin N. Kim (ORCID: https://orcid.org/0000-0003-4713-8552)
- Robert Gilkeson (ORCID: https://orcid.org/0000-0002-5931-8123)
- Tao Hu (ORCID: https://orcid.org/0000-0001-5267-7804)
- Hao Wu (ORCID: https://orcid.org/0000-0001-6993-8863)
- Ju Hwan Lee (ORCID: https://orcid.org/0000-0001-6762-4588)
Institutions
- University Hospitals of Cleveland (US)
- Case Western Reserve University (US)
- University of Edinburgh (GB)
Publication Details
- Journal
- Journal of cardiovascular computed tomography
- Published
- 2025-02-04
- DOI
- https://doi.org/10.1016/j.jcct.2025.01.007
- Citations
- 4
- Primary Topic
- Cardiac Imaging and Diagnostics
- Type
- article
- Field-Weighted Citation Impact
- 5.44
Funders
- Case Western Reserve University
- National Institutes of Health
- National Heart, Lung, and Blood Institute