Methods and Reproducibility of Liver Fat Measurement Using 3-Dimensional Liver Segmentation From Noncontrast Computed Tomography in EVAPORATE Cohort

Abstract

Objective Nonalcoholic fatty liver disease not only shares multiple risk factors with cardiovascular disease but also independently predicts its increased risk and related outcomes. Here, we evaluate reproducibility of 3-dimensional (3D) liver volume segmentation method to identify fatty liver on noncontrast cardiac computed tomography (CT) and compare measures with previously validated 2-dimensional (2D) segmentation CT criteria for the measurement of liver fat. Methods The study included 68 participants enrolled in the EVAPORATE trial and underwent serial noncontrast cardiac CT. Liver attenuation < 40 Hounsfield units (HU) was used for diagnosing fatty liver, as done in the MESA study. Two-dimensional and 3D segmentation of the liver were performed by Philips software. Bland-Altman plot analysis was used to assess reproducibility. Results Interreader reproducibility of 3D liver mean HU measurements was 96% in a sample of 111 scans. Reproducibility of 2D and 3D liver mean HU measurements was 93% in a sample of 111 scans. Reproducibility of change in 2D and 3D liver mean HU was 94% in 68 scans. Kappa, a measure of agreement in which the 2D and 3D measures both identified fatty liver, was excellent at 96.4% in 111 scans. Conclusions Fatty liver can be reliably diagnosed and measured serially in a stable and reproducible way by 3D liver segmentation of noncontrast cardiac CT scans. Future studies need to explore the sensitivity and stability of measures for low liver fat content by 3D segmentation, over the current 2D methodology. This measure can serve as an imaging biomarker to understand mechanistic correlations between atherosclerosis, fatty liver, and cardiovascular disease risk.