基于深度学习的冠状动脉钙化积分对2型糖尿病患者冠心病的预测价值

Predictive value of deep learning-based coronary artery calcification score for coronary artery disease in type 2 diabetes mellitus

目的探究基于深度学习(DL)的冠状动脉钙化积分(CACS)对2型糖尿病(T2DM)患者阻塞性冠心病和非钙化斑块/混合斑块的预测价值。方法连续回顾性纳入2012年12月至2019年12月接受CACS扫描和冠状动脉CT血管成像(CCTA)的424例T2DM患者,并收集临床风险因素和斑块特征。斑块成分分为钙化、非钙化和混合斑块。阻塞性冠心病定义为最大直径狭窄率≥50%。采用基于DL的自动化方法计算CACS。采用单因素和多因素逻辑回归筛选有统计学意义的因素,并计算比值比(OR)。用受试者工作特征(ROC)曲线评价预测性能。结果CACS增加与更高的CCTA阻塞性冠心病概率相关(与CACS=0对比,CACS为1~99、100~299、300~999调整后OR分别为2.22、6.18、16.98,P值分别为0.009、<0.001、<0.001)。CACS预测阻塞性冠心病的曲线下面积(AUC)为0.764。对比CACS=0,CACS增加与非钙化斑块/混合斑块风险增加有关(CACS为1~99、100~299、300~999调整后OR分别为2.75、4.76、5.29,P值分别为0.001、<0.001、<0.001)。CACS预测非钙化斑块/混合斑块的AUC为0.688。基于DL的CACS自动测量时间为1.17 min,低于手动测量时间1.73 min(P<0.001)。结论基于DL的CACS具有预测T2DM患者阻塞性冠心病、非钙化斑块/混合斑块的价值,经济、高效,对临床诊疗具有重要价值。

Objective To explore the predictive value of deep learning(DL)-based coronary artery calcification score(CACS)for obstructive coronary artery disease(CAD)and noncalcified plaque/mixed plaque in type 2 diabetes mellitus(T2DM).Methods Forty hundred and twenty-four consecutive T2DM patients who accepted CACS scan and coronary CT angiography(CCTA)from December 2012 to December 2019 were included retrospectively,with clinical risk factors and plaque features collected.Plaque composition was classified as calcified,non-calcified or mixed plaque.Obstructive CAD was defined as maximum diameter stenosis≥50%.CACS was calculated with a fully automated method based on DL.Univariate and multivariate logistic regressions were applied to select statistically significant factors and the odds ratios(ORs)were measured.Receiver operating characteristic(ROC)curve was evaluated to assess the predictive performance.Results Increased CACS was associated with a significantly higher odds of obstructive CAD in CCTA(adjusted ORs were 2.22,6.18 and 16.98 for CACS=1-99,100-299,300-999 vs.CACS=0,and P values were 0.009,<0.001,<0.001 respectively).The area under ROC curve(AUC)of CACS to predict obstructive CAD was 0.764.Compared with 0,increased CACS was associated with increased risk of non-calcified/mixed plaque(adjusted ORs were 2.75,4.76,5.29 for CACS=1-99,100-299,300-999 respectively and P values were 0.001,<0.001,<0.001 respectively).The AUC of CACS to predict non-calcified/mixed plaque was 0.688.It took 1.17 min to perform automated measurement of CACS based on DL in total,which was significantly less than manual measurement of 1.73 min(P<0.001).Conclusion DL-based CACS can predict obstructive CAD and non-calcified plaque/mixed plaque in T2DM,which is economical and efficient,and has important value for clinical diagnosis and treatment.