Wang Fanghu, Yuan Hui, Lv Jieqin, Han Xu, Zhou Zidong, Lu Wantong, Lu Lijun, Jiang Lei
PET Center, Department of Nuclear Medicine, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, .
Department of Nuclear Medicine, The Second Affiliated Hospital of Guangzhou University of Chinese Medicine, .
Nucl Med Commun. 2024 Jan 1;45(1):35-44. doi: 10.1097/MNM.0000000000001782. Epub 2023 Oct 12.
Rest-stress SPECT myocardial perfusion imaging (MPI) is widely used to evaluate coronary artery disease (CAD). We aim to evaluate stress-only versus rest-stress MPI in diagnosing CAD by machine learning (ML).
A total of 276 patients with suspected CAD were randomly divided into training (184 patients) and validation (92 patients) cohorts. Variables extracted from clinical, physiological, and rest-stress SPECT MPI were screened. Stress-only and rest-stress MPI using ML were established and compared using the training cohort. Then the diagnostic performance of two models in diagnosing myocardial ischemia and infarction was evaluated in the validation cohort.
Six ML models based on stress-only MPI selected summed stress score, summed wall thickness score of stress%, and end-diastolic volume of stress as key variables and performed equally good as rest-stress MPI in detecting CAD [area under the curve (AUC): 0.863 versus 0.877, P = 0.519]. Furthermore, stress-only MPI showed a reasonable prediction of reversible deficit, as shown by rest-stress MPI (AUC: 0.861). Subsequently, nomogram models using the above-stated stress-only MPI variables showed a good prediction of CAD and reversible perfusion deficit in training and validation cohorts.
Stress-only MPI demonstrated similar diagnostic performance compared with rest-stress MPI using 6 ML algorithms. Stress-only MPI with ML models can diagnose CAD and predict ischemia from scar.
静息-负荷单光子发射计算机断层扫描心肌灌注成像（MPI）广泛用于评估冠状动脉疾病（CAD）。我们旨在通过机器学习（ML）评估仅负荷与静息-负荷MPI在诊断CAD中的作用。
总共276例疑似CAD患者被随机分为训练组（184例患者）和验证组（92例患者）。筛选从临床、生理和静息-负荷SPECT MPI中提取的变量。使用训练组建立并比较仅负荷和静息-负荷MPI的ML模型。然后在验证组中评估两种模型在诊断心肌缺血和梗死方面的诊断性能。
基于仅负荷MPI的6个ML模型选择了负荷总分、负荷百分比的总壁厚度评分和负荷末期舒张容积作为关键变量，在检测CAD方面与静息-负荷MPI表现同样出色[曲线下面积（AUC）：0.863对0.877，P = 0.519]。此外，仅负荷MPI对可逆性缺损显示出合理的预测，如静息-负荷MPI所示（AUC：0.861）。随后，使用上述仅负荷MPI变量的列线图模型在训练组和验证组中对CAD和可逆性灌注缺损显示出良好的预测。
使用6种ML算法时，仅负荷MPI与静息-负荷MPI相比显示出相似的诊断性能。带有ML模型的仅负荷MPI可诊断CAD并从瘢痕中预测缺血。
