Use of Artificial Intelligence-Assisted Histopathology for Evaluation of Sex-Specific Progression and Regression of Hepatocellular Carcinoma Related to Metabolic Dysfunction-Associated Fatty Liver Disease

Background/Objectives: Sex-specific differences in metabolic dysfunction-associated fatty liver disease (MAFLD)-related hepatocellular carcinoma (HCC) remain poorly understood. This study aimed to clarify sex-associated disparities in disease progression and recovery using a diethylnitrosamine (DEN) plus Western diet/fructose-induced murine model combined with artificial intelligence (AI)-assisted histological analysis. Methods: Male and female C57BL/6J mice received a single diethylnitrosamine injection and were fed a Western diet/fructose regimen for 38 weeks, followed by an 8-week recovery period on standard chow. Serum biochemical parameters were measured, and liver histology was assessed using second harmonic generation/two-photon excitation fluorescence (SHG/TPEF) microscopy. Steatosis and fibrosis were quantified within tumor and adjacent non-tumor regions using AI-based image analysis. Results: Male mice developed more severe disease phenotypes, including greater tumor burden and higher serum alanine aminotransferase levels, compared with females. Following dietary recovery, female mice showed substantial reductions in tumor number and hepatic steatosis, particularly in non-tumor regions; in contrast, male mice demonstrated only minimal improvement. AI-assisted quantification confirmed considerable regression of both steatosis and fibrosis in females and moderate fibrosis improvement in both sexes. Conclusions: These findings indicate sexual dimorphism in the progression and regression of MAFLD-related HCC, with females exhibiting enhanced metabolic and histological recovery. The results underscore the importance of considering sex as a biological variable in preclinical metabolic dysfunction–associated fatty liver disease-related hepatocellular carcinoma research and highlight the value of AI-enhanced imaging for precise, objective evaluation of liver histology.