Deep learning-based multimodal model for predicting on-treatment histological outcomes in chronic hepatitis B-associated advanced liver fibrosis

BACKGROUND Chronic hepatitis B (CHB)-related liver fibrosis is a major driver of severe hepatic complications, with substantial interindividual heterogeneity in histological outcomes after antiviral therapy. Histopathological images contain rich biological information, but deep learning (DL) models for predicting on-treatment histological outcomes in CHB-related liver fibrosis are scarce. AIM To develop and independently validate a DL-based multimodal model to predict fibrosis reversal following antiviral therapy using histopathological images and clinical features. METHODS This multicenter study included 238 patients from 14 institutions who received antiviral therapy and had both hematoxylin and eosin (HE) and Masson-stained liver biopsy slides available. The training, validation, and test cohorts comprised 114, 50, and 74 patients, respectively. Convolutional neural network models were independently developed using HE- and Masson-stained images, and subsequently combined with clinical features to construct a multimodal predictive model for evaluating fibrosis regression after antiviral treatment. RESULTS The HE model achieved areas under the receiver operating characteristic curves (AUCs) of 0.657 and 0.615 in the validation and test sets, respectively. The Masson model yielded AUCs of 0.727 and 0.676 for the corresponding sets. The clinical model exhibited AUCs of 0.658 in the validation set and 0.588 in the test set. The multimodal fusion model demonstrated enhanced discriminatory performance, reaching AUCs of 0.741 and 0.694 in the validation and test sets, respectively. Subgroup analysis showed robust predictive capacity in patients with progressive fibrosis (AUC = 0.779) and in those with hepatitis B e antigen-positive status (AUC = 0.755). Gradient-weighted class activation mapping revealed that the model focused primarily on key histological features associated with non-reversal, including hepatocyte degeneration, disorganized hepatic cords, and thick-bridging fibrous septa. CONCLUSION Digital pathology and clinical-based DL accurately predict fibrosis regression after antiviral therapy in CHB-related liver fibrosis, particularly in patients with progressive fibrosis and hepatitis B e antigen-positive status, supporting personalized treatment strategies.