Abstract
We compared the prognostic value of myocardial perfusion imaging (MPI) by conventional- (C-) single-photon emission computed tomography (SPECT) and cadmium-zinc-telluride- (CZT-) SPECT in a cohort of patients with suspected or known coronary artery disease (CAD) using machine learning (ML) algorithms. A total of 453 consecutive patients underwent stress MPI by both C-SPECT and CZT-SPECT. The outcome was a composite end point of all-cause death, cardiac death, nonfatal myocardial infarction, or coronary revascularization procedures whichever occurred first. ML analysis performed through the implementation of random forest (RF) and k-nearest neighbors (KNN) algorithms proved that CZT-SPECT has greater accuracy than C-SPECT in detecting CAD. For both algorithms, the sensitivity of CZT-SPECT (96% for RF and 60% for KNN) was greater than that of C-SPECT (88% for RF and 53% for KNN). A preliminary univariate analysis was performed through Mann-Whitney tests separately on the features of each camera in order to understand which ones could distinguish patients who will experience an adverse event from those who will not. Then, a machine learning analysis was performed by using Matlab (v. 2019b). Tree, KNN, support vector machine (SVM), Naïve Bayes, and RF were implemented twice: first, the analysis was performed on the as-is dataset; then, since the dataset was imbalanced (patients experiencing an adverse event were lower than the others), the analysis was performed again after balancing the classes through the Synthetic Minority Oversampling Technique. According to KNN and SVM with and without balancing the classes, the accuracy (p value = 0.02 and p value = 0.01) and recall (p value = 0.001 and p value = 0.03) of the CZT-SPECT were greater than those obtained by C-SPECT in a statistically significant way. ML approach showed that although the prognostic value of stress MPI by C-SPECT and CZT-SPECT is comparable, CZT-SPECT seems to have higher accuracy and recall.
Highlights
Risk stratification by noninvasive cardiac imaging has become increasingly important to optimize management and outcome in patients with coronary artery disease (CAD) [1]
Previous research indicated that stress singlephoton emission computed tomography (SPECT) myocardial perfusion imaging (MPI) has been the most widely used nuclear cardiac imaging technique for the noninvasive assessment of cardiac disease, including the prognosis and choice of the most appropriate treatment strategies for patients with CAD [2]
Previous studies showed that CZT-SPECT findings can be used for risk stratification of patients referred to MPI for suspected or known CAD
Summary
Risk stratification by noninvasive cardiac imaging has become increasingly important to optimize management and outcome in patients with coronary artery disease (CAD) [1]. Conventional- (C-) SPECT systems utilize sodium iodide crystals and parallel-hole collimators. This approach presents some technical limits; for instance, we can mention extended imaging time, low spatial resolution, and large doses of radiopharmaceuticals [3]. These limitations have been overcome with the introduction of gamma cameras with semiconductor cadmium-zinctelluride (CZT) allowed to directly convert radiation into electric signals, bringing an improvement in image accuracy and acquisition time [4, 5]. Previous studies showed that CZT-SPECT findings can be used for risk stratification of patients referred to MPI for suspected or known CAD. Yokota et al [7] showed that the prognostic value of normal stress-only CZTSPECT is at least comparable and may be even better than that of normal C-SPECT [7]
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