Epicardial Adipose Tissue CT Radiomics Improves Acute Coronary Syndrome Prediction Beyond Coronary Artery Calcium Score

Objectives: To determine if global epicardial adipose tissue (EAT) radiomics, derived from non-contrast coronary artery calcium (CAC) scans, improves acute coronary syndrome (ACS) prediction beyond traditional risk factors (TRFs) and Agatston score (AS) in individuals without angina. Methods: We retrospectively analyzed 2020 subjects without angina who underwent CAC scans from 2016 to 2019, among whom 76 patients developed acute coronary syndrome (ACS) during a follow-up period until December 2023. One-to-one propensity score matching (PSM) based on TRFs (age, sex, BMI, smoking, diabetes, hypertension, dyslipidemia) and Agatston score ranks (ASR) created 76 ACS and 76 matched non-ACS subjects. A radiomics model was built using 5-fold cross-validation on the matched cohort and tested on the entire unmatched cohort. Statistical tests included AUC comparison. Results: PSM effectively mitigated disparities of TRFs and ASR. The radiomics model achieved AUCs of 0.91 ± 0.01 and 0.89 ± 0.03 for the training and matched test sets, respectively, and 0.89 ± 0.03 on the unmatched cohort. The radiomics score significantly improved ACS prediction over TRF and CAC models (p < 0.001) in both matched and unmatched cohorts. Conclusions: Global EAT radiomic phenotypes from conventional CAC scan improve ACS risk stratification beyond TRFs and AS in individuals without angina. In contrast to PCAT on CCTA, our simple approach appears to be suitable for large-scale applications using preventative CAC scans.