1. Comparing the Prognostic Value of Stress Myocardial Perfusion Imaging by Conventional and Cadmium-Zinc Telluride Single-Photon Emission Computed Tomography through a Machine Learning Approach
- Author
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Carmela Nappi, Mario Petretta, Leandro Donisi, Teresa Mannarino, Roberta Green, Emilia Zampella, Vincenzo Sannino, Andrea Genova, Alessia Giordano, Alberto Cuocolo, Valeria Cantoni, Giuseppe Cesarelli, Valeria Gaudieri, Wanda Acampa, Giovanni De Simini, Roberta Assante, Adriana D'Antonio, Carlo Ricciardi, Cantoni, Valeria, Green, Roberta, Ricciardi, Carlo, Assante, Roberta, Donisi, Leandro, Zampella, Emilia, Cesarelli, Giuseppe, Nappi, Carmela, Sannino, Vincenzo, Gaudieri, Valeria, Mannarino, Teresa, Genova, Andrea, De Simini, Giovanni, Giordano, Alessia, D'Antonio, Adriana, Acampa, Wanda, Petretta, Mario, Cuocolo, Alberto, and D’Antonio, Adriana
- Subjects
Male ,Article Subject ,Computer applications to medicine. Medical informatics ,R858-859.7 ,Coronary Artery Disease ,Single-photon emission computed tomography ,Machine learning ,computer.software_genre ,General Biochemistry, Genetics and Molecular Biology ,Machine Learning ,Myocardial perfusion imaging ,Naive Bayes classifier ,medicine ,Humans ,Myocardial infarction ,Aged ,Tomography, Emission-Computed, Single-Photon ,Univariate analysis ,General Immunology and Microbiology ,medicine.diagnostic_test ,business.industry ,Applied Mathematics ,Myocardial Perfusion Imaging ,Computational Biology ,General Medicine ,Middle Aged ,Prognosis ,medicine.disease ,Random forest ,Support vector machine ,Zinc ,Modeling and Simulation ,Exercise Test ,Female ,Neural Networks, Computer ,Artificial intelligence ,Tellurium ,business ,computer ,Algorithms ,Emission computed tomography ,Research Article ,Cadmium - 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.
- Published
- 2021