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.
Aged
Algorithms
Cadmium
Computational Biology
Coronary Artery Disease
/ diagnostic imaging
Exercise Test
/ methods
Female
Humans
Machine Learning
Male
Middle Aged
Myocardial Perfusion Imaging
/ methods
Neural Networks, Computer
Prognosis
Tellurium
Tomography, Emission-Computed, Single-Photon
/ methods
Zinc
Journal
Computational and mathematical methods in medicine
ISSN: 1748-6718
Titre abrégé: Comput Math Methods Med
Pays: United States
ID NLM: 101277751
Informations de publication
Date de publication:
2021
2021
Historique:
received:
08
06
2021
revised:
30
09
2021
accepted:
05
10
2021
entrez:
26
10
2021
pubmed:
27
10
2021
medline:
29
1
2022
Statut:
epublish
Résumé
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
Identifiants
pubmed: 34697554
doi: 10.1155/2021/5288844
pmc: PMC8541857
doi:
Substances chimiques
CdZnTe
0
Cadmium
00BH33GNGH
Zinc
J41CSQ7QDS
Tellurium
NQA0O090ZJ
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5288844Informations de copyright
Copyright © 2021 Valeria Cantoni et al.
Déclaration de conflit d'intérêts
The authors declare that they have no conflict of interests.
Références
Artif Intell Med. 2020 Apr;104:101822
pubmed: 32499001
Health Informatics J. 2020 Sep;26(3):2181-2192
pubmed: 31969043
Sci Rep. 2020 Nov 18;10(1):20127
pubmed: 33208913
IEEE Trans Neural Netw Learn Syst. 2018 May;29(5):1774-1785
pubmed: 28422666
JACC Cardiovasc Imaging. 2018 Jul;11(7):1000-1009
pubmed: 29055639
JACC Cardiovasc Imaging. 2009 Mar;2(3):273-82
pubmed: 19356571
J Nucl Med. 1964 Jul;5:515-31
pubmed: 14216630
Front Cardiovasc Med. 2021 Feb 02;8:614204
pubmed: 33634169
J Digit Imaging. 2020 Aug;33(4):879-887
pubmed: 32314070
J Nucl Cardiol. 2020 Sep 30;:
pubmed: 33000403
Eur Heart J. 2013 Oct;34(38):2949-3003
pubmed: 23996286
JACC Cardiovasc Imaging. 2008 Mar;1(2):156-63
pubmed: 19356422
Comput Methods Programs Biomed. 2019 Oct;179:104992
pubmed: 31443858
J Nucl Cardiol. 2021 Jun;28(3):888-897
pubmed: 31222530
J Nucl Cardiol. 2020 May 18;:
pubmed: 32424676
J Thorac Cardiovasc Surg. 2008 Jul;136(1):46-51
pubmed: 18603052
Eur J Transl Myol. 2020 Apr 01;30(1):8892
pubmed: 32499893
Sci Rep. 2020 Feb 18;10(1):2863
pubmed: 32071412
Circulation. 2012 Oct 16;126(16):2020-35
pubmed: 22923432
Radiol Cardiothorac Imaging. 2021 Feb 25;3(1):e200512
pubmed: 33778661
Sci Rep. 2017 Aug 29;7(1):9841
pubmed: 28851984
Doc Ophthalmol. 2003 Sep;107(2):131-6
pubmed: 14661903
Comput Methods Programs Biomed. 2020 Jun;189:105343
pubmed: 31981760
J Bioinform Comput Biol. 2005 Apr;3(2):185-205
pubmed: 15852500
J Nucl Cardiol. 2004 Mar-Apr;11(2):171-85
pubmed: 15052249
J Healthc Inf Manag. 2005 Spring;19(2):64-72
pubmed: 15869215
Eur Heart J Cardiovasc Imaging. 2016 Jun;17(6):591-5
pubmed: 26985078
Future Sci OA. 2021 Mar 29;7(6):FSO698
pubmed: 34046201
Eur J Nucl Med Mol Imaging. 2016 Feb;43(2):296-301
pubmed: 26392197
J Nucl Cardiol. 2017 Feb;24(1):245-251
pubmed: 27510176