To investigate the diagnostic value of joint PET myocardial perfusion and metabolic imaging for vascular stenosis in patients with suspected obstructive coronary artery disease (CAD). Eighty-eight patients (53 and 35 applied for training and validation, respectively) with suspected obstructive CAD were referred to N-NH PET/CT myocardial perfusion imaging (MPI) and F-FDG PET/CT myocardial metabolic imaging (MMI) with available coronary angiography for analysis. One semi-quantitative indicator summed rest score (SRS) and five quantitative indicators, namely, perfusion defect extent (EXT), total perfusion deficit (TPD), myocardial blood flow (MBF), scar degree (SCR), and metabolism-perfusion mismatch (MIS), were extracted from the PET rest MPI and MMI scans. Different combinations of indicators and seven machine learning methods were used to construct diagnostic models. Diagnostic performance was evaluated using the sum of four metrics (noted as sumScore), namely, area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity. In univariate analysis, MIS outperformed other individual indicators in terms of sumScore (2.816-3.042 vs 2.138-2.908). In multivariate analysis, support vector machine (SVM) consisting of three indicators (MBF, SCR, and MIS) achieved the best performance (AUC 0.856, accuracy 0.810, sensitivity 0.838, specificity 0.757, and sumScore 3.261). This model consistently achieved significantly higher AUC compared with the SRS method for four specific subgroups (0.897, 0.839, 0.875, and 0.949 vs 0.775, 0.606, 0.713, and 0.744; = 0.041, 0.005, 0.034 0.003, respectively). The joint evaluation of PET rest MPI and MMI could improve the diagnostic performance for obstructive CAD. The multivariate model (MBF, SCR, and MIS) combined with SVM outperformed other methods.

中文翻译：

---

联合PET心肌灌注与代谢显像对阻塞性冠状动脉疾病患者血管狭窄的诊断价值评价

探讨联合PET心肌灌注和代谢显像对疑似阻塞性冠状动脉疾病（CAD）患者血管狭窄的诊断价值。88 名疑似阻塞性 CAD 患者（分别申请培训和验证的有 53 名和 35 名）被转诊至 N-NH PET/CT 心肌灌注成像 (MPI) 和 F-FDG PET/CT 心肌代谢成像 (MMI)，并获得可用的冠状动脉造影进行分析。一项半定量指标累加休息评分（SRS）和五项定量指标，即灌注缺损程度（EXT）、总灌注缺损（TPD）、心肌血流量（MBF）、疤痕程度（SCR）和代谢-灌注不匹配(MIS)，从 PET 休息 MPI 和 MMI 扫描中提取。使用不同的指标组合和七种机器学习方法来构建诊断模型。使用四个指标的总和（记为 sumScore）评估诊断性能，即受试者工作特征曲线下面积 (AUC)、准确性、敏感性和特异性。在单变量分析中，MIS 在 sumScore 方面优于其他单项指标（2.816-3.042 vs 2.138-2.908）。在多变量分析中，由MBF、SCR和MIS三个指标组成的支持向量机（SVM）取得了最佳性能（AUC 0.856，准确度0.810，灵敏度0.838，特异性0.757，sumScore 3.261）。与四个特定亚组的 SRS 方法相比，该模型始终获得显着更高的 AUC（分别为 0.897、0.839、0.875 和 0.949 vs 0.775、0.606、0.713 和 0.744；= 0.041、0.005、0.034 0.003）。PET静息MPI和MMI的联合评估可以提高阻塞性CAD的诊断性能。多变量模型（MBF、SCR 和 MIS）与 SVM 相结合优于其他方法。