Automated quantification of epicardial adipose tissue (EAT) in coronary CT angiography; comparison with manual assessment and correlation with coronary artery disease

Abstract Background Epicardial adipose tissue (EAT) is emerging as a risk factor for coronary artery disease (CAD). Objective The aim of this study was to determine the applicability and efficiency of automated EAT quantification. Methods EAT volume was assessed both manually and automatically in 157 patients undergoing coronary CT angiography. Manual assessment consisted of a short-axis–based manual measurement, whereas automated assessment on both contrast and non–contrast-enhanced data sets was achieved through novel prototype software. Duration of both quantification methods was recorded, and EAT volumes were compared with paired samples t test. Correlation of volumes was determined with intraclass correlation coefficient; agreement was tested with Bland–Altman analysis. The association between EAT and CAD was estimated with logistic regression. Results Automated quantification was significantly less time consuming than automated quantification (17 ± 2 seconds vs 280 ± 78 seconds; P P r  = 0.76; P Conclusion Automated EAT quantification is a quick method to estimate EAT and may serve as a predictor for CAD presence and severity.