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

Abstract

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.