Hamasaki Hiroshi, Arimura Hidetaka, Yamasaki Yuzo, Yamamoto Takayuki, Fukata Mitsuhiro, Matoba Tetsuya, Kato Toyoyuki, Ishigami Kousei
Division of Medical Quantum Science, Department of Health Sciences, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Division of Radiology, Department of Medical Technology, Kyushu University Hospital, 3-1-1, Maidashi, Higashi-ku, Fukuoka, 812-8582, Japan.
Phys Eng Sci Med. 2025 Mar;48(1):167-180. doi: 10.1007/s13246-024-01503-z. Epub 2024 Dec 30.
This study proposed noninvasive machine-learning models for the detection of lesion-specific ischemia (LSI) in patients with stable angina with intermediate stenosis severity based on coronary computed tomography (CT) angiography. This single-center retrospective study analyzed 76 patients (99 vessels) with stable angina who underwent coronary CT angiography (CCTA) and had intermediate stenosis severity (40-69%) on invasive coronary angiography. LSI, defined as a resting full-cycle ratio < 0.86 or fractional flow reserve ≤ 0.80, was determined in 40 patients (46 vessels) using a hybrid resting full-cycle ratio-fractional flow reserve strategy. The resting full-cycle ratio and/or fractional flow reserve were measured using invasive coronary angiography as references for functional severity indices of coronary stenosis in the machine-learning models. LSI detection models were constructed using noninvasive machine-learning models that predicted the resting full-cycle ratio and fractional flow reserve by feeding machine-learning models with image features extracted from CCTA. The diagnostic performance of the proposed LSI detection models was assessed using a nested 10-fold cross-validation test. The LSI detection models with the highest diagnostic performance achieved an accuracy of 0.88 (95% CI: 0.81, 0.94), sensitivity of 0.78 (95% CI: 0.70, 0.86) and specificity of 0.96 (95% CI: 0.92, 1.00) on a vessel basis and 0.88 (95% CI: 0.81, 0.95), 0.80 (95% CI: 0.70, 0.86) and 0.97 (95% CI: 0.92, 1.00), respectively, on a patient basis. These findings suggest that LSI detection models with features extracted from CCTA can noninvasively detect LSI in patients with stable angina with intermediate stenosis severity.
本研究基于冠状动脉计算机断层扫描（CT）血管造影，提出了用于检测中度狭窄严重程度的稳定型心绞痛患者病变特异性缺血（LSI）的无创机器学习模型。这项单中心回顾性研究分析了76例接受冠状动脉CT血管造影（CCTA）且在有创冠状动脉造影中狭窄严重程度为中度（40%-69%）的稳定型心绞痛患者（99支血管）。采用静息全周期比值<0.86或血流储备分数≤0.80的混合策略，在40例患者（46支血管）中确定了LSI。使用有创冠状动脉造影测量静息全周期比值和/或血流储备分数，作为机器学习模型中冠状动脉狭窄功能严重程度指标的参考。通过将从CCTA中提取的图像特征输入机器学习模型来预测静息全周期比值和血流储备分数，构建了LSI检测模型。使用嵌套的10倍交叉验证测试评估所提出的LSI检测模型的诊断性能。诊断性能最高的LSI检测模型在血管层面的准确率为0.88（95%CI：0.81，0.94），敏感性为0.78（95%CI：0.70，0.86），特异性为0.96（95%CI：0.92，1.00）；在患者层面的准确率为0.88（95%CI：0.81，0.95），敏感性为0.80（95%CI：。这些结果表明，具有从CCTA中提取特征的LSI检测模型可以无创地检测中度狭窄严重程度的稳定型心绞痛患者的LSI。
