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首页> 美国卫生研究院文献>Journal of Nuclear Medicine >Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study
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Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study

机译：立式仰卧式高效SPECT心肌灌注成像的深度学习分析对阻塞性冠状动脉疾病的预测：多中心研究

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Combined analysis of SPECT myocardial perfusion imaging (MPI) performed with a solid-state camera on patients in 2 positions (semiupright, supine) is routinely used to mitigate attenuation artifacts. We evaluated the prediction of obstructive disease from combined analysis of semiupright and supine stress MPI by deep learning (DL) as compared with standard combined total perfusion deficit (TPD). >Methods: 1,160 patients without known coronary artery disease (64% male) were studied. Patients underwent stress 99mTc-sestamibi MPI with new-generation solid-state SPECT scanners in 4 different centers. All patients had on-site clinical reads and invasive coronary angiography correlations within 6 mo of MPI. Obstructive disease was defined as at least 70% narrowing of the 3 major coronary arteries and at least 50% for the left main coronary artery. Images were quantified at Cedars-Sinai. The left ventricular myocardium was segmented using standard clinical nuclear cardiology software. The contour placement was verified by an experienced technologist. Combined stress TPD was computed using sex- and camera-specific normal limits. DL was trained using polar distributions of normalized radiotracer counts, hypoperfusion defects, and hypoperfusion severities and was evaluated for prediction of obstructive disease in a novel leave-one-center-out cross-validation procedure equivalent to external validation. During the validation procedure, 4 DL models were trained using data from 3 centers and then evaluated on the 1 center left aside. Predictions for each center were merged to have an overall estimation of the multicenter performance. >Results: 718 (62%) patients and 1,272 of 3,480 (37%) arteries had obstructive disease. The area under the receiver operating characteristics curve for prediction of disease on a per-patient and per-vessel basis by DL was higher than for combined TPD (per-patient, 0.81 vs. 0.78; per-vessel, 0.77 vs. 0.73; P < 0.001). With the DL cutoff set to exhibit the same specificity as the standard cutoff for combined TPD, per-patient sensitivity improved from 61.8% (TPD) to 65.6% (DL) (P < 0.05), and per-vessel sensitivity improved from 54.6% (TPD) to 59.1% (DL) (P < 0.01). With the threshold matched to the specificity of a normal clinical read (56.3%), DL had a sensitivity of 84.8%, versus 82.6% for an on-site clinical read (P = 0.3). >Conclusion: DL improves automatic interpretation of MPI as compared with current quantitative methods.
机译：通常使用固态照相机对2个位置（半直立，仰卧）的患者进行SPECT心肌灌注成像（MPI）的组合分析，以减轻衰减伪影。我们通过深度学习（DL）与标准合并总灌注不足（TPD）相比较的半直立和仰卧压力MPI组合分析评估了梗阻性疾病的预测。 >方法：研究了1,160例无已知冠状动脉疾病的患者（男性占64％）。患者在4个不同的中心接受了新一代固态SPECT扫描仪接受的应力 99m Tc-sestamibi MPI。所有患者在MPI的6个月内均具有现场临床读数和有创冠状动脉造影相关性。阻塞性疾病定义为3条主要冠状动脉狭窄至少70％，左主冠状动脉狭窄至少50％。在Cedars-Sinai对图像进行量化。使用标准的临床核心脏病学软件对左心室心肌进行分割。轮廓放置已由经验丰富的技术人员验证。使用性别和相机特定的正常极限值来计算综合压力TPD。使用归一化放射性示踪剂计数，灌注不足和灌注严重程度的极性分布对DL进行了训练，并通过一种与外部验证等效的新型“留一中心出”交叉验证程序对DL进行了阻塞性疾病预测预测。在验证过程中，使用来自3个中心的数据训练了4个DL模型，然后在剩下的1个中心进行了评估。合并每个中心的预测以对多中心性能进行整体评估。 >结果：718名患者（62％）和3,480条动脉中的1,272名（37％）患有阻塞性疾病。用DL预测每位患者和每支血管的疾病的接收器工作特性曲线下的面积高于联合TPD（每位患者0.81对0.78；每支血管，0.77对0.73； P <0.001）。通过将DL临界值设置为与联合TPD标准临界值相同的特异性，每位患者的敏感度从61.8％（TPD）改善到65.6％（DL）（P <0.05），每个血管的敏感度从54.6％提高（TPD）至59.1％（DL）（P <0.01）。阈值与正常临床读数的特异性相匹配（56.3％），DL的敏感度为84.8％，而现场临床读数的敏感度为82.6％（P = 0.3）。 >结论：与当前的定量方法相比，DL改进了MPI的自动解释。

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