Why the study?
Does an automated deep learning-radiomics pipeline using non-contrast calcium score CT accurately detect non-calcified coronary plaques in patients with suspected CAD?
Population
Patients undergoing coronary CT angiography for suspected CAD from two medical sites
Comparison
Lesion vs control groups and radiomics models based on coronary artery, PCAT, or combined ROIs
Design
Retrospective two-center diagnostic study
Key result
An automated deep learning-radiomics pipeline using non-contrast calcium score CT detected non-calcified coronary plaques with moderate to good diagnostic performance, achieving AUCs ranging from 0.700 to 0.855 across datasets.
Authors
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May support AI development for non-calcified plaque detection on calcium scoring CT; leaves open prospective validation before clinical use.
Observational (n=1,745)
Yes
Does an automated deep learning-radiomics pipeline using non-contrast calcium score CT accurately detect non-calcified coronary plaques in patients with suspected CAD?
An automated deep learning and radiomics pipeline using non-contrast calcium score CT can effectively detect non-calcified coronary plaques, offering a potential tool for large-scale CAD screening.
Effect estimate: AUC 0.700-0.855
Chen et al. (2026) conducted an observational in Suspected coronary artery disease (n=1,745). Automated deep learning-radiomics pipeline using non-contrast CACS vs. Coronary CT angiography (reference standard) was evaluated on Detection of non-calcified coronary plaques (AUC 0.700-0.855). An automated deep learning-radiomics pipeline using non-contrast calcium score CT detected non-calcified coronary plaques with moderate to good diagnostic performance, achieving AUCs ranging from 0.700 to 0.855 across datasets.