用普通CT扫描数据，机器学习预测心肌缺血，准确率超98%。
Quantitative coronary calcification analysis for prediction of myocardial ischemia using non-contrast CT calcium scoring
- 基于钙化特征与临床数据的机器学习模型，融合钙化组学新指标
- 灵敏度79.2%，精确率达98.9%，显著优于传统评分方法
- 适合心血管风险筛查，尤其关注钙化斑块分布的临床医生
非增强CT钙化评分（CTCS）是心血管风险分层的有效工具。本研究在1375名患者中，分析了同时接受非增强CTCS和双嘧达莫负荷心肌正电子发射断层扫描的影像数据。共评估74个变量，包括临床信息、Agatston评分及钙化组学特征。通过XGBoost结合SHAP值筛选相关特征，采用5折交叉验证训练并评估预测模型。在987例患者中，89例（9%）存在心肌缺血。最终模型包含年龄、Agatston评分及8个钙化组学特征，取得精度98.9±3.0%、灵敏度79.2±8.4、F1分数87.7±5.3%。相比仅用临床变量或临床+Agatston评分的模型，加入钙化组学特征显著提升预测性能（p<0.05）。值得注意的是，尽管根据SHAP分析动脉钙化数量为最低排名特征，但在逻辑回归中其与心肌缺血关联最强（优势比3.63，95%置信区间2.80-4.77，p<0.00001）。研究提出一种基于常规非增强CTCS的机器学习方法，钙化组学特征可显著提升预测能力，有助于更便捷的心血管风险评估。
原文摘要 · Abstract (English)
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.
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