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. 2023 Mar 1;13(3):1524-1536.
doi: 10.21037/qims-22-758. Epub 2023 Feb 6.

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

Bao Liu  1   2 , Wenji Yu  1   2 , Feifei Zhang  1   2 , Yunmei Shi  1   2 , Le Yang  1   2 , Qi Jiang  1   2 , Yufeng Wang  1   2 , Yuetao Wang  1   2
Affiliations

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

Bao Liu et al. Quant Imaging Med Surg. .

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, 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.

Keywords: Machine learning (ML); coronary artery calcium score (CACS); coronary artery disease (CAD); myocardial perfusion imaging (MPI); single photon emission computed tomography (SPECT).

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Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-22-758/coif). The authors have no other conflicts of interest to declare.

Figures

Figure 1
Figure 1
Flowchart of patient recruitment and study design. CAD, coronary artery disease; SPECT, single photon emission computerized tomography; MPI, myocardial perfusion imaging; MI, myocardial infarction; PCI, percutaneous coronary intervention; CABG, coronary artery bypass grafting; CAG, coronary angiography; CACS, coronary artery calcium score.
Figure 2
Figure 2
Violin plot comparing characteristics of patients between groups. BMI, body mass index; EDV, end diastolic volume; ESV, end systolic volume; LVEF, left ventricular ejection fraction; PFR, peak filling rate; SRS, summed rest score; SMS, summed motion score; CACS, coronary artery calcium score; CAD, coronary artery disease.
Figure 3
Figure 3
Correlation heatmap of the features used. The numbers represent the Spearman correlation coefficients between the two features. SMS, summed motion score; SRS, summed rest score; HPL, hyperlipidemia; HTN, hypertension; CACS, coronary artery calcium score.
Figure 4
Figure 4
Comparison of ROC curves of 8 ML algorithms. (A) ML performance in the training group. (B) ML performance in the test group. ROC, receiver operator characteristic curve; ML, machine learning; AUC, the area under the receiver operator characteristic curve; KNN, K-nearest neighbor; SVM, support vector machine. Rpart, recursive partitioning and regression trees; XGBoost, extreme gradient boosting; AdaBoost, adaptive boosting.
Figure 5
Figure 5
Diagnostic performance of 8 ML algorithms. (A) The sensitivity and specificity of ML algorithms in the training group. (B) The sensitivity and specificity of ML algorithms in the test group. ML, machine learning; KNN, K-nearest neighbor; SVM, support vector machine; Rpart, recursive partitioning and regression trees; XGBoost, extreme gradient boosting; AdaBoost, adaptive boosting.
Figure 6
Figure 6
Importance of features for different algorithms. (A) Importance of features for the random forest. (B) Importance of features for the XGBoost. (C) Importance of features for the Logistic. (D) Importance of features for the Rpart. SMS, summed motion score; CACS, coronary artery calcium score; SRS, summed rest score; XGBoost, extreme gradient boosting; Rpart, recursive partitioning and regression trees.
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