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 non-contrast 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.

Keywords: Coronary artery calcium (CAC); coronary computed tomography angiography (CCTA); non-contrast cardiac computed tomography (CT).

Conflict of interest statement

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-21-775). WW and SW are employees of GE Healthcare China and provided data analysis support. The other authors have no conflicts of interest to declare.

2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.

Figures

Figure 1

Flow diagram of our quantification of Agatston CAC scores from CCTA scans. The first stage was deep learning-based coronary artery segmentation. Then, a fixed threshold was applied to detect and quantify calcium plaque. Finally, the CAC scores were obtained. CCTA, coronary computed tomography angiography; CAC, coronary artery calcium.

Figure 2

The architecture of coronary artery segmentation. The schematic illustrates the coronary artery segmentation process with dataset preparation, pre- and postprocessing to optimize the segmentation results, network architecture, training progress, and test progress.

Figure 3

Example patients. Example patients for coronary artery segmentation and CAC quantification results. A 77-year-old female patient with calcium lesions in the RCA (green), LAD (pink), and LMCx (blue). (A) Illustrates the CAC on the non-contrast CT scan (red plaque). (B) Illustrates the CAC on the CCTA scan (green plaque). (C) Illustrates the total coronary segmentation results. The white plaque on the coronary artery tree represents calcium. The table below shows the standard Agatston CAC score on non-contrast CT scans and CCTA-CAC score on CCTA scans. Radiation dose comparisons are also shown in the table. RCA, right coronary artery; LAD, left anterior descending; LMCx, left main and left circumflex arteries; CCTA, coronary computed tomography angiography; CAC, coronary artery calcium; CT, computed tomography; CCTA-CAC score, CCTA Agatston CAC score.

Figure 4

Regression and correlation between the non-contrast CT Agatston CAC score and the CCTA-CAC score (threshold 650 HU). (A) Presents the full range scatter plot. (B) A magnified view of the standard Agatston CAC score up to 1,000. Using the appropriate formula, we were able to better observe the correlations between the two sets of data and understand the trend of changes between them. CCTA-CAC score, CCTA-Agatston CAC score; CAC, coronary artery calcium; CCTA, coronary computed tomography angiography; CT, computed tomography.

Figure 5

The Bland-Altman analysis of the Agatston CAC scores as assessed with CCTA-CAC scores (threshold 650 HU). (A) The full-range scatter plot. (B) A magnified view of the standard Agatston CAC score up to 1,000; 95% CI of mean difference, 95% CI of limits of agreement, and the regression line of difference are included. CCTA-CAC score, CCTA-Agatston CAC score; CCTA, coronary computed tomography angiography; CAC, coronary artery calcium; CI, confidence interval.