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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

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...

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Autores principales: Liu, Bao, Yu, Wenji, Zhang, Feifei, Shi, Yunmei, Yang, Le, Jiang, Qi, Wang, Yufeng, Wang, Yuetao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006131/
https://www.ncbi.nlm.nih.gov/pubmed/36915324
http://dx.doi.org/10.21037/qims-22-758
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author Liu, Bao
Yu, Wenji
Zhang, Feifei
Shi, Yunmei
Yang, Le
Jiang, Qi
Wang, Yufeng
Wang, Yuetao
author_facet Liu, Bao
Yu, Wenji
Zhang, Feifei
Shi, Yunmei
Yang, Le
Jiang, Qi
Wang, Yufeng
Wang, Yuetao
author_sort Liu, Bao
collection PubMed
description 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.
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spelling pubmed-100061312023-03-12 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 Liu, Bao Yu, Wenji Zhang, Feifei Shi, Yunmei Yang, Le Jiang, Qi Wang, Yufeng Wang, Yuetao Quant Imaging Med Surg Original Article 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. AME Publishing Company 2023-02-06 2023-03-01 /pmc/articles/PMC10006131/ /pubmed/36915324 http://dx.doi.org/10.21037/qims-22-758 Text en 2023 Quantitative Imaging in Medicine and Surgery. All rights reserved. https://creativecommons.org/licenses/by-nc-nd/4.0/Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) .
spellingShingle Original Article
Liu, Bao
Yu, Wenji
Zhang, Feifei
Shi, Yunmei
Yang, Le
Jiang, Qi
Wang, Yufeng
Wang, Yuetao
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
title 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
title_full 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
title_fullStr 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
title_full_unstemmed 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
title_short 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
title_sort 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
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006131/
https://www.ncbi.nlm.nih.gov/pubmed/36915324
http://dx.doi.org/10.21037/qims-22-758
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