Cargando…
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
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) algorithm...
Autores principales: | , , , , , , , , , , , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Hindawi
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8541857/ https://www.ncbi.nlm.nih.gov/pubmed/34697554 http://dx.doi.org/10.1155/2021/5288844 |
_version_ | 1784589331354091520 |
---|---|
author | 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 |
author_facet | 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 |
author_sort | Cantoni, Valeria |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8541857 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Hindawi |
record_format | MEDLINE/PubMed |
spelling | pubmed-85418572021-10-24 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 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 Comput Math Methods Med Research Article 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. Hindawi 2021-10-16 /pmc/articles/PMC8541857/ /pubmed/34697554 http://dx.doi.org/10.1155/2021/5288844 Text en Copyright © 2021 Valeria Cantoni et al. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Article 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 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 |
title | 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 |
title_full | 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 |
title_fullStr | 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 |
title_full_unstemmed | 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 |
title_short | 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 |
title_sort | 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 |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8541857/ https://www.ncbi.nlm.nih.gov/pubmed/34697554 http://dx.doi.org/10.1155/2021/5288844 |
work_keys_str_mv | AT cantonivaleria comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT greenroberta comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT ricciardicarlo comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT assanteroberta comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT donisileandro comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT zampellaemilia comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT cesarelligiuseppe comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT nappicarmela comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT sanninovincenzo comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT gaudierivaleria comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT mannarinoteresa comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT genovaandrea comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT desiminigiovanni comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT giordanoalessia comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT dantonioadriana comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT acampawanda comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT petrettamario comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach AT cuocoloalberto comparingtheprognosticvalueofstressmyocardialperfusionimagingbyconventionalandcadmiumzinctelluridesinglephotonemissioncomputedtomographythroughamachinelearningapproach |