Automated quantification of epicardial adipose tissue using CT angiography: evaluation of a prototype software.

OBJECTIVES: This study evaluated the performance of a novel automated software tool for epicardial fat volume (EFV) quantification compared to a standard manual technique at coronary CT angiography (cCTA).
METHODS: cCTA data sets of 70 patients (58.6 ± 12.9 years, 33 men) were retrospectively analysed using two different post-processing software applications. Observer 1 performed a manual single-plane pericardial border definition and EFVM segmentation (manual approach). Two observers used a software program with fully automated 3D pericardial border definition and EFVA calculation (automated approach). EFV and time required for measuring EFV (including software processing time and manual optimization time) for each method were recorded. Intraobserver and interobserver reliability was assessed on the prototype software measurements. T test, Spearman's rho, and Bland-Altman plots were used for statistical analysis.
RESULTS: The final EFVA (with manual border optimization) was strongly correlated with the manual axial segmentation measurement (60.9 ± 33.2 mL vs. 65.8 ± 37.0 mL, rho = 0.970, P < 0.001). A mean of 3.9 ± 1.9 manual border edits were performed to optimize the automated process. The software prototype required significantly less time to perform the measurements (135.6 ± 24.6 s vs. 314.3 ± 76.3 s, P < 0.001) and showed high reliability (ICC > 0.9).
CONCLUSIONS: Automated EFVA quantification is an accurate and time-saving method for quantification of EFV compared to established manual axial segmentation methods. KEY POINTS: • Manual epicardial fat volume quantification correlates with risk factors but is time-consuming. • The novel software prototype automates measurement of epicardial fat volume with good accuracy. • This novel approach is less time-consuming and could be incorporated into clinical workflow.