用胸部CT扫描数据预测冠心病，准确率超90%
Machine learning prediction of obstructive coronary artery disease using opportunistic coronary calcium and epicardial fat assessments from CT calcium scoring scans
- 从CT图像中提取钙化与心外膜脂肪特征，用机器学习建模
- 模型敏感度83.1%，特异度93.8%，零钙化患者也可识别病变
- 适合低至中风险人群，有望减少有创检查
非增强计算机断层扫描钙化评分（CTCS）是一种成本效益高的影像学手段，广泛用于检测冠状动脉钙化。本研究旨在开发一种基于机器学习的先进框架，利用CTCS图像中冠状动脉钙化和心外膜脂肪的定量分析来预测阻塞性冠状动脉疾病（obstructive CAD）。研究纳入了来自SCOT-HEART临床试验的1,324名患者，他们均接受了CTCS和冠状动脉CT血管造影。我们从图像中提取并分析了广泛的特征，包括24项临床变量、189项钙化组学（calcium-omics）特征和211项心外膜脂肪组学（epicardial fat-omics）特征。通过结合CatBoost算法与SHapley Additive exPlanation（SHAP）值进行特征选择，最终筛选出14个最具预测性的特征。其中前两大特征来自脂肪组学，其余12项源自钙化组学。优化后的模型表现出稳健的预测能力：敏感度为83.1±4.6%，特异度为93.8±1.7%，准确率为85.3±2.0%，F1得分为73.9±3.3%。包含钙化组学与脂肪组学数据显著提升了预测性能。值得注意的是，该模型在不同冠状动脉钙化评分的患者中均表现可靠，甚至能识别出钙化评分为零但存在阻塞性病变的病例。这一创新方法有望提升临床决策质量，并可能减少对对比增强或侵入性诊断程序的依赖，尤其适用于低至中风险人群。
原文摘要 · Abstract (English)
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.
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