Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans

2026-05-20

AI Summary

This study proposes a non-invasive and accurate approach to predict obstructive coronary artery disease (CAD) using non-contrast CT-based coronary calcium scoring. Leveraging clinical data from 1,324 patients in the SCOT-HEART trial, along with 189 coronary calcification radiomic features and 211 epicardial fat radiomic features, the authors developed a predictive model using the CatBoost gradient boosting algorithm and SHAP interpretability analysis. This work represents the first systematic integration of calcification and fat radiomics, substantially enhancing predictive performance—even among patients with a calcium score of zero. The final model identified 14 key features and achieved a sensitivity of 83.1%, specificity of 93.8%, and overall accuracy of 85.3% across the entire cohort, demonstrating strong potential to reduce reliance on invasive diagnostic procedures.

Abstract

Non-contrast computed tomography calcium scoring (CTCS) is a cost-effective imaging modality widely used to detect coronary artery calcifications. This study aimed to develop an advanced machine learning framework that utilizes quantitative analyses of coronary calcium and epicardial fat from CTCS images to predict obstructive coronary artery disease (CAD). The study population consisted of 1,324 patients from the SCOT-HEART clinical trial who underwent both CTCS and coronary CT angiography. We extracted and analyzed a broad range of features, including 24 clinical variables, 189 calcium-omics, and 211 epicardial fat-omics features from the CTCS images. Feature selection was conducted using the CatBoost algorithm combined with SHapley Additive exPlanation (SHAP) values. Predictive modeling utilized the CatBoost gradient boosting method, focusing on the most informative features. From an initial set of 424 candidate features, 14 were identified as most predictive through the CatBoost-SHAP method. The top two predictive features originated from fat-omics, with the remaining 12 features derived from calcium-omics. The optimized model achieved robust predictive capabilities, demonstrating a sensitivity of 83.1+/-4.6%, specificity of 93.8+/-1.7%, accuracy of 85.3+/-2.0%, and an F1 score of 73.9+/-3.3%. Inclusion of calcium-omics and fat-omics data significantly improved predictive performance. Notably, the model also showed reliable predictive accuracy in patients with diverse coronary calcium scores, including cases with obstructive CAD despite a zero-calcium score. This innovative approach holds promise for improving clinical decision-making and potentially reducing dependence on contrast-enhanced or invasive diagnostic procedures, particularly within low-to intermediate-risk patient groups.

Problem

• obstructive coronary artery disease
• coronary calcium
• epicardial fat
• CT calcium scoring
• machine learning

Innovation

• machine learning
• coronary artery disease
• calcium-omics
• epicardial fat-omics
• non-contrast CT