Monte Carlo modeling of hepatic steatosis based on stereology and spatial distribution of fat droplets. (May 2023) - Wang, Jinyang
Highlights: Organization of fat deposits may affect the evaluation of hepatic steatosis. Hepatic steatosis is modeled according to spatial distribution of fat droplets. The model demonstrates striking similarity with respect to real liver samples. The model can help understand MRI signal behaviors for human liver steatosis. Abstract: Background and Objective: To model hepatic steatosis in adult humans with non-alcoholic fatty liver disease based on stereology and spatial distribution of fat droplets from liver biopsy specimens. Methods: Histological analysis was performed on 30 adult human liver biopsy specimens with varying degrees of steatosis. Morphological features of fat droplets were characterized by gamma distribution function (GDF) in both two-dimensional (2D) and three-dimensional (3D) spaces from three aspects: 1) size distribution indicating non-uniformity of fat droplets in radius; 2) nearest neighbor distance distribution indicating heterogeneous accumulation (i.e., clustering) of fat droplets; 3) regional anisotropy indicating inter-regional variability in fat fraction (FF). To generalize the morphological description of hepatic steatosis to different FFs, correlation analysis was performed among the estimated GDF parameters and FFs for all specimens. Finally, Monte Carlo modeling of hepatic steatosis was developed to simulate fat droplet distribution in tissue. Results: Morphological features, including size and nearest neighbor distance in 2D and 3D spaces as well as regional anisotropy, statistically captured the distribution of fat droplets by the GDF fit ( R 2 > 0.54). The estimated GDF parameters (i.e., scale and shape parameters) and FFs were well correlated, with R 2 > 0.55. In addition, simulated 3D liver morphological models demonstrated similar sections to real histological samples both visually and quantitatively. Conclusions: The morphology of hepatic steatosis is well characterized by stereology and spatial distribution of fat droplets. Simulated models demonstrate similar appearances to real histological samples. Furthermore, the model may help understand MRI signal behavior in the presence of liver steatosis.