Prediction of Obstructive Coronary Artery Disease Using Coronary Calcification and Epicardial Adipose Tissue Assessments from CT Calcium Scoring Scans

Authors

Juhwan Lee, Case Western Reserve UniversityFollow
Tao Hu, Case Western Reserve UniversityFollow
Ammar Hoori, Case Western Reserve UniversityFollow
Hao Wu, Case Western Reserve UniversityFollow
Justin N. Kim, Case Western Reserve UniversityFollow
Robert Gilkeson, Case Western Reserve UniversityFollow
Sanjay Rajagopalan, Case Western Reserve UniversityFollow
David L. Wilson, Case Western Reserve UniversityFollow

Document Type

Article

Publication Date

2-4-2025

Abstract

Background: Low-cost/no-cost non-contrast CT calcium scoring (CTCS) exams can provide direct evidence of coronary atherosclerosis. In this study, using features from CTCS images, we developed a novel machine learning model to predict obstructive coronary artery disease (CAD), as defined by the coronary artery disease-reporting and data system (CAD-RADS). Methods: This study analyzed 1324 patients from the SCOT-HEART trial who underwent both CTCS and CT angiography. Obstructive CAD was defined as CAD-RADS 4A-5, while CAD-RADS 0–3 were considered non-obstructive CAD. We analyzed clinical, Agatston-score-derived, and epicardial fat-omics features to predict obstructive CAD. The most predictive features were selected using elastic net logistic regression and used to train a CatBoost model. Model performance was evaluated using 1000 repeated five-fold cross-validation and survival analyses to predict major adverse cardiovascular event (MACE) and revascularization. Generalizability was assessed using an external validation set of 2316 patients for survival predictions. Results: Among the 1324 patients, obstructive CAD was identified in 334 patients (25.2 ​%). Elastic net regression identified the top 14 features (5 clinical, 2 Agatston-score-derived, and 7 fat-omics). The proposed method achieved excellent performance for classifying obstructive CAD, with an AUC of 90.1 ​± ​0.9 ​% and sensitivity/specificity/accuracy of 83.5 ​± ​5.5 ​%/93.7 ​± ​1.9 ​%/82.4 ​± ​2.0 ​%. The inclusion of Agatston-score-derived and fat-omics features significantly improved classification performance. Survival analyses showed that both actual and predicted obstructive CAD significantly differentiated patients who experienced MACE and revascularization. Conclusions: We developed a novel machine learning model to predict obstructive CAD from non-contrast CTCS scans. Our findings highlight the potential clinical benefits of CTCS imaging in identifying patients likely to benefit from advanced imaging.

Keywords

CAD-RADS, classification, computed tomography calcium scoring, fat-omics, machine learning, obstructive coronary artery disease

Language

English

Publication Title

Journal of Cardiovascular Computed Tomography

Grant

K01HL171795

Rights

© 2025 The Authors. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/BY-NC-ND/4.0/), which permits non-commercial copying and redistribution of the material in any medium or format, provided the original work is not changed in any way and is properly cited.

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.

Recommended Citation

Lee, Juhwan; Hu, Tao; Hoori, Ammar; Wu, Hao; Kim, Justin N.; Gilkeson, Robert; Rajagopalan, Sanjay; and Wilson, David L., "Prediction of Obstructive Coronary Artery Disease Using Coronary Calcification and Epicardial Adipose Tissue Assessments from CT Calcium Scoring Scans" (2025). Faculty Scholarship. 1373.
https://commons.case.edu/facultyworks/1373