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Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer

This study aimed to demonstrate clinical feasibility of deep learning (DL)-based fully automated coronary artery calcium (CAC) scoring software using non-electrocardiogram (ECG)-gated chest computed tomography (CT) from patients with cancer. Overall, 913 patients with colorectal or gastric cancer wh...

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Autores principales: Choi, Joo Hyeok, Cha, Min Jae, Cho, Iksung, Kim, William D., Ha, Yera, Choi, Hyewon, Lee, Sun Hwa, You, Seng Chan, Chang, Jee Suk
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530804/
https://www.ncbi.nlm.nih.gov/pubmed/36203468
http://dx.doi.org/10.3389/fonc.2022.989250
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author Choi, Joo Hyeok
Cha, Min Jae
Cho, Iksung
Kim, William D.
Ha, Yera
Choi, Hyewon
Lee, Sun Hwa
You, Seng Chan
Chang, Jee Suk
author_facet Choi, Joo Hyeok
Cha, Min Jae
Cho, Iksung
Kim, William D.
Ha, Yera
Choi, Hyewon
Lee, Sun Hwa
You, Seng Chan
Chang, Jee Suk
author_sort Choi, Joo Hyeok
collection PubMed
description This study aimed to demonstrate clinical feasibility of deep learning (DL)-based fully automated coronary artery calcium (CAC) scoring software using non-electrocardiogram (ECG)-gated chest computed tomography (CT) from patients with cancer. Overall, 913 patients with colorectal or gastric cancer who underwent non-contrast-enhanced chest CT between 2013 and 2015 were included. Agatston scores obtained by manual segmentation of CAC on chest CT were used as reference. Reliability of automated CAC score acquisition was evaluated using intraclass correlation coefficients (ICCs). The agreement for cardiovascular disease (CVD) risk stratification was assessed with linearly weighted k statistics. ICCs between the manual and automated CAC scores were 0.992 (95% CI, 0.991 and 0.993, p<0.001) for total Agatston scores, 0.863 (95% CI, 0.844 and 0.880, p<0.001) for the left main, 0.964 (95% CI, 0.959 and 0.968, p<0.001) for the left anterior descending, 0.962 (95% CI, 0.956 and 0.966, p<0.001) for the left circumflex, and 0.980 (95% CI, 0.978 and 0.983, p<0.001) for the right coronary arteries. The agreement for cardiovascular risk was excellent (k=0.946, p<0.001). Current DL-based automated CAC software showed excellent reliability for Agatston score and CVD risk stratification using non-ECG gated CT scans and might allow the identification of high-risk cancer patients for CVD.
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spelling pubmed-95308042022-10-05 Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer Choi, Joo Hyeok Cha, Min Jae Cho, Iksung Kim, William D. Ha, Yera Choi, Hyewon Lee, Sun Hwa You, Seng Chan Chang, Jee Suk Front Oncol Oncology This study aimed to demonstrate clinical feasibility of deep learning (DL)-based fully automated coronary artery calcium (CAC) scoring software using non-electrocardiogram (ECG)-gated chest computed tomography (CT) from patients with cancer. Overall, 913 patients with colorectal or gastric cancer who underwent non-contrast-enhanced chest CT between 2013 and 2015 were included. Agatston scores obtained by manual segmentation of CAC on chest CT were used as reference. Reliability of automated CAC score acquisition was evaluated using intraclass correlation coefficients (ICCs). The agreement for cardiovascular disease (CVD) risk stratification was assessed with linearly weighted k statistics. ICCs between the manual and automated CAC scores were 0.992 (95% CI, 0.991 and 0.993, p<0.001) for total Agatston scores, 0.863 (95% CI, 0.844 and 0.880, p<0.001) for the left main, 0.964 (95% CI, 0.959 and 0.968, p<0.001) for the left anterior descending, 0.962 (95% CI, 0.956 and 0.966, p<0.001) for the left circumflex, and 0.980 (95% CI, 0.978 and 0.983, p<0.001) for the right coronary arteries. The agreement for cardiovascular risk was excellent (k=0.946, p<0.001). Current DL-based automated CAC software showed excellent reliability for Agatston score and CVD risk stratification using non-ECG gated CT scans and might allow the identification of high-risk cancer patients for CVD. Frontiers Media S.A. 2022-09-20 /pmc/articles/PMC9530804/ /pubmed/36203468 http://dx.doi.org/10.3389/fonc.2022.989250 Text en Copyright © 2022 Choi, Cha, Cho, Kim, Ha, Choi, Lee, You and Chang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Choi, Joo Hyeok
Cha, Min Jae
Cho, Iksung
Kim, William D.
Ha, Yera
Choi, Hyewon
Lee, Sun Hwa
You, Seng Chan
Chang, Jee Suk
Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer
title Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer
title_full Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer
title_fullStr Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer
title_full_unstemmed Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer
title_short Validation of deep learning-based fully automated coronary artery calcium scoring using non-ECG-gated chest CT in patients with cancer
title_sort validation of deep learning-based fully automated coronary artery calcium scoring using non-ecg-gated chest ct in patients with cancer
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9530804/
https://www.ncbi.nlm.nih.gov/pubmed/36203468
http://dx.doi.org/10.3389/fonc.2022.989250
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