Detecting obstructive coronary artery disease with machine learning: rest-only gated single photon emission computed tomography myocardial perfusion imaging combined with coronary artery calcium score and cardiovascular risk factors

Abstract

Background: The rest-only single photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has low diagnostic performance for obstructive coronary artery disease (CAD). Coronary artery calcium score (CACS) is strongly associated with obstructive CAD. The aim of this study was to investigate the performance of rest-only gated SPECT MPI combined with CACS and cardiovascular risk factors in diagnosing obstructive CAD through machine learning (ML). Methods: We enrolled 253 suspected CAD patients who underwent the 1-stop rest-only SPECT MPI and computed tomography (CT) scan due to stress test-related contraindications. Myocardial perfusion and wall motion were assessed using quantitative perfusion SPECT + quantitative gated SPECT (QPS + QGS) automated quantification software. The Agatston algorithm was used to calculate CACS. The clinical data of patients, including cardiovascular risk factors, were collected. Based on feature selection and clinical experience, 8 factors were identified as modeling variables. Subsequently, patients were divided randomly into 2 groups: the training (70%) and test (30%) groups. The performance of 8 supervised ML algorithms was evaluated in the training and test groups.Results: Obstructive CAD was diagnosed by coronary angiography in 94 (37.2%, 94/253) patients. In the training group, the area under the receiver operator characteristic (ROC) curve (AUC) of the random forest was the highest, and the AUCs of Logistic, extreme gradient boosting (XGBoost), support vector machine (SVM), and adaptive boosting (AdaBoost) were all above 0.9. In the test group, the AUC of recursive partitioning and regression trees (Rpart) was the highest (0.911). Rpart and Naïve Bayes had the highest accuracy (0.840). Rpart had a sensitivity and specificity of 0.851 and 0.821, respectively; Naïve Bayes had a sensitivity and specificity of 0.809 and 0.893, respectively. Next was Logistic, with an accuracy of 0.827, a sensitivity of 0.872, and a specificity of 0.750. The random forest and XGBoost algorithms also had high accuracy, which was 0.813 for each algorithm.Conclusions: Rest-only SPECT MPI combined with CACS and cardiovascular risk factors using an ML algorithm to detect obstructive CAD is feasible. Among the algorithms validated in the test group, Rpart, ^ ORCID: 0000-0003-2859-8625. Resting image acquisition and analysis99m Technetium-sestamibi ( 99m Tc-MIBI) was administered intravenously at rest. Scanning was started after 60-90 minutes of rest. A dual-head 90 o gamma camera (Symbia T16; Siemens Medical Systems, Erlangen, Germany) equipped with a parallel-hole collimator with low energy and high resolution was used for image acquisition.Naïve Bayes, XGBoost, Logistic, and random forest are all highly accurate for diagnosing obstructive CAD.The application of ML in resting MPI and CACS may be used for screening obstructive CAD.

Association between Cardiovascular Risk Factors and High-risk Features in Myocardial Perfusion Imaging: A Multicenter Study

“… 12 This imaging method is applied to evaluate coronary blood flow in stress and rest conditions by detailing the spread of radioactive tracers. 13 MPI is an accessible, noninvasive, cost-effective modality for evaluating the extent of CAD before coronary angiography, which is the gold standard, thus reducing the number of unnecessary angiographic examinations and enabling appropriate treatments. 14 As we found in the present study, MPI in the form of single-photon emission computed tomography (SPECT) and gated increases specificity and sensitivity to 0.86 and 0.90, respectively.…”