Modeling Liver Fibrosis Progression in Patients With Viral Hepatitis Using the Machine Learning Tool Subtype and...

Background and aim The natural progression of liver fibrosis and its association with biomarker changes have not been fully established in the literature. This study aimed to investigate liver fibrosis progression in patients with hepatitis B virus (HBV) and hepatitis C virus (HCV) infection using a novel machine learning tool called 'Subtype and Stage Inference (SuStaIn).' SuStaIn can identify disease progression patterns and subgroups from cross-sectional biomarker data. Methods This single-center retrospective study included 168 consecutive patients (mean age, 67.0 years ± 13.2; 91 male), comprising 29 with HBV, 50 with HCV, and 89 controls. All patients underwent gadoxetic acid-enhanced magnetic resonance imaging between January 2019 and December 2019. Imaging biomarkers measured were quantitative liver-spleen contrast ratio (Q-LSC), contrast index of liver-muscle signal intensity (CEI), and right liver lobe to the total liver volume ratio (RV/TV), while the serum biomarkers were fibrosis-4 index (FIB-4) and aspartate aminotransferase to platelet ratio index (APRI). SuStaIn was applied using z-scored biomarkers derived from each biomarker relative to the control group. Results The most likely ordering of the biomarker conversion in liver fibrosis progression was determined to be APRI, FIB-4, CEI, Q-LSC, and RV/TV. This sequence was observed to be consistent in all patients infected with either HBV or HCV, regardless of the virus type. Conclusions Using SuStaIn, new insights into liver fibrosis progression in patients infected with either HBV or HCV were achieved: abnormalities in serum biomarkers occurred first, followed by a decrease in hepatic uptake of gadoxetic acid, ultimately resulting in right lobe atrophy.