Predicting high-risk plaque features using epicardial adipose tissue assessments from non-contrast CT calcium scoring scan

High-risk plaque features in coronary computed tomography angiography (CCTA), such as positive remodeling (PR), have been strongly associated with an increased risk of acute coronary events. This study aimed to develop a novel machine learning model to predict PR from non-contrast CT calcium scoring (CTCS) scans. This study included 1,324 patients who underwent both CCTA and CTCS. PR was defined as an outer vessel diameter that exceeded the mean diameter of the segments immediately proximal and distal to the plaque by 10% in CCTA images. We analyzed various clinical characteristics, coronary artery calcium (Agatston) score, and novel epicardial adipose tissue features (fat-omics), encompassing 211 radiomic features, including morphological, spatial, and intensity parameters. We employed elastic net regression to select the most predictive features, which were then used to train a CatBoost classification model. The predictive value of our method was assessed through 1,000 repetitions of five-fold cross validation. Among the 1,769 patients, PR was identified in 429 patients (24.3%). Using the top 11 features, including 4 clinical, Agatston score, and 6 fat-omics features, selected by the elastic net, our model achieved excellent classification of PR, with a sensitivity of 79.3±2.7%, a specificity of 89.3±1.8%, and accuracy of 82.1±2.1%. Among all employed methods, the CatBoost method showed the best classification results. This method shows promise in facilitating informed clinical decision-making and potentially reducing the need for invasive and costly testing among patients at low risk.