An automated quantification method for the Agatston coronary artery calcium score on coronary computed tomography angiography

Abstract

Background: A coronary artery calcium (CAC) score can provide supplementary information for predicting the risk of cardiovascular disease (CVD). Although CAC is clinically measured with non-contrast cardiac computed tomography (CT), coronary CT angiography (CCTA) may also be used, allowing for the simultaneous evaluation of coronary artery vessels and calcified plaques. This study proposes a method for the automated quantification of the Agatston CAC score from CCTA and compares our method's performance with that of non-contrast cardiac CT.Methods: Sixty-two patients were selected from a clinical registry and divided into four CAC categories. They underwent both non-contrast cardiac CT and CCTA. The Agatston CAC score derived from noncontrast cardiac CT (standard Agatston CAC score) was used as the reference standard. Calcifications were automatically identified and quantified using different thresholds after a deep learning-based coronary artery segmentation model pretrained on CCTA images. Comparisons were made between the standard Agatston CAC score and the CCTA-based Agatston CAC score (CCTA-CAC score) on a per-patient and per-vessel basis. Spearman's rank-order correlation coefficient (R) and intra-class correlation (ICC) values were used to calculate the correlation between the two methods.Results: After comparison, the optimal lower threshold in CCTA-CAC score calculations was found to be 650 Hounsfield units (HU). Using this threshold on a per-patient basis, the automatically computed CCTA-CAC score showed a high correlation (R =0.959; P<0.01) and ICC (R =0.8219; P<0.01) with the standard Agatston CAC score. On a per-vessel basis, the standard Agatston CAC score was also highly correlated with the CCTA-CAC score (R =0.889; P<0.01 and ICC =0.717; P<0.01). Of the 62 patients enrolled, 47 (76%)were classified into the same cardiovascular risk category using the CCTA-CAC score quantification method as when the standard Agatston CAC score was used. Agreement within the CAC categories was also good (kappa =0.7560).Conclusions: Fully automated quantification of the Agatston CAC score on CCTA images is feasible and shows a high correlation with the reference standard. This method could simplify the quantification procedure and has the potential to reduce the radiation dose and save time by eliminating the non-contrast cardiac CT stage.

Novel fast FFR derived from coronary CT angiography based on static first-pass algorithm: a comparison study

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Coronary computed tomography angiography analysis using artificial intelligence for stenosis quantification and stent segmentation: a multicenter study

“…Advancements in artificial intelligence (AI) technology, particularly deep learning, have substantially improved medical image analysis ( 10 ). In recent years, AI has been employed to identify coronary stenosis using CCTA scans, aiding clinicians in enhancing diagnostic efficiency and precision in CAD ( 11 - 17 ). Notably, Choi et al ( 14 ) were the first to perform external validation of an AI product approved by the USA Food and Drug Administration (FDA) for CCTA-supported analysis, marking a critical milestone in AI’s transition from research to clinical practice.…”

A comparison of computed tomography imaging with histopathology in the sensitivity and correlation of evaluating coronary arterial calcification

“…Calcification of the coronary artery is a significant independent risk factor in predicting cardiac events and death in patients with coronary heart disease (1)(2)(3)(4)(5). Microcalcification, spotty calcification, and calcified nodules are closely related to acute coronary syndrome (6)(7)(8)(9).…”