Cardiometabolic phenotyping from Coronary Artery Calcium (CAC) scans predicts obstructive and high-risk plaques on Coronary CT Angiography (CCTA): an AI-CVD study within Miami Heart Study
K Atlas, C Zhang, A Reeves, R Mirjalili, A Azimi, M MozafaryBazargany, A Ghaffari Jolfayi, A Hashemi, H Ghoshouni, H Zarei, H Mohammadi, F Fayad, D Yankelevitz, N Wong, M NaghaviAbstract
Background
CAC scans quantify calcified plaques but do not directly measure non-calcified plaques (NCP) or high-risk plaques (HRP) described in CCTA reports. Whether AI-derived cardiometabolic biomarkers from CAC scans can predict CCTA-defined plaque phenotypes is unknown.
Purpose
To evaluate whether comprehensive AI-based phenotyping of CAC scans can discriminate obstructive and high-risk plaques, and whether these models outperform CAC score alone.
Methods
We applied FDA-cleared AI-CVD deep learning algorithms to CAC scans of 1,259 asymptomatic participants from the Miami Heart Study (aged 40 to 65 years, 48.7% female) who underwent paired CAC and CCTA imaging. AI-CVD algorithms extracted cardiometabolic biomarkers from CAC scans, including calcium scores, plaque burden indices, pericoronary adipose tissue textural features, and metabolic biomarkers such as epicardial and visceral adipose tissue volume, skeletal muscle mass and density, and liver attenuation within the available field of view of the CAC scans.
Results
AI-CVD demonstrated significant improvements over the traditional Agatston CAC score for detecting obstructive stenosis (AUC 0.943 vs. 0.881), HRP (AUC 0.779 vs. 0.626), and NCP (AUC 0.771 vs. 0.679). These findings suggest that non-contrast CAC imaging contains potential vascular and metabolic phenotype information beyond traditional Agatston scoring, enabling prediction of plaque characteristics not directly visualized on CAC scans.
Conclusion
Comprehensive AI-based cardiometabolic phenotyping of routine CAC scans enables cross-sectional prediction of obstructive and high-risk plaque phenotypes typically assessed only with contrast-enhanced CCTA. These findings suggest that non-contrast CAC imaging functions as a multidimensional cardiometabolic phenotyping tool, expanding its clinical utility beyond calcium quantification and potentially enabling more precise, risk-guided referral to CCTA.ROC curveROC curve