Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring
Summary
This paper presents a machine learning framework using non-contrast CT calcium scoring and calcium-omics features to predict myocardial ischemia, achieving a precision of 98.9% and an F1 score of 87.7%.
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# Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring Source: [https://arxiv.org/abs/2605.21745](https://arxiv.org/abs/2605.21745) [View PDF](https://arxiv.org/pdf/2605.21745) > Abstract:Non\-contrast computed tomography calcium scoring \(CTCS\) is widely recognized as an effective tool for cardiovascular risk stratification\. This study aimed to develop a novel machine learning framework for predicting myocardial ischemia from routine non\-contrast CTCS scans using quantitative coronary calcium assessment\. This study analyzed 1,375 patients who underwent both non\-contrast CTCS and regadenoson stress cardiac positron emission tomography myocardial perfusion imaging within one year at University Hospitals Cleveland Medical Center\. A total of 74 variables, including clinical variables, Agatston score, and calcium\-omics features, were evaluated\. Relevant features were identified using XGBoost with Shapley Additive exPlanations \(SHAP\)\. Predictive models were trained and evaluated using 5\-fold cross\-validation\. Among 987 patients, 89 \(9%\) were positive for myocardial ischemia\. The final model incorporated the Agatston score, eight calcium\-omics features, and age\. The proposed model achieved a precision of 98\.9\+/\-3\.0%, sensitivity of 79\.2\+/\-8\.4, and F1 score of 87\.7\+/\-5\.3%\. The addition of calcium\-omics features significantly improved predictive performance compared with models using clinical variables alone or clinical variables with the Agatston score \(p<0\.05\)\. Interestingly, the number of calcified arteries, despite being the lowest\-ranked feature based on SHAP analysis, showed the strongest association with myocardial ischemia in logistic regression analysis \(odds ratio: 3\.63, 95% confidence interval: 2\.80\-4\.77, p<0\.00001\)\. We developed a machine learning approach for predicting myocardial ischemia using routinely acquired non\-contrast CTCS scans\. Calcium\-omics features provided incremental predictive value beyond conventional risk factors and Agatston scoring and may support more accessible cardiovascular risk stratification\. ## Submission history From: Juhwan Lee \[[view email](https://arxiv.org/show-email/6ff99c21/2605.21745)\] **\[v1\]**Wed, 20 May 2026 21:16:52 UTC \(892 KB\)
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