Digital pathology-based prognostic model for hepatocellular carcinoma: Integrating pathomics signatures with clinical parameters for recurrence prediction and biological interpretation.

[BACKGROUND] Hepatocellular carcinoma (HCC) remains a therapeutic challenge due to high post-resection recurrence rates and heterogeneous outcomes. We developed and validated a digital pathology-based prognostic model combining pathomics signatures with clinical parameters to predict recurrence and elucidate biological mechanisms.

[METHODS] In this multicenter retrospective study, 294 HCC patients (training set: n = 198; validation set: n = 96) undergoing curative hepatectomy were analyzed. Pathomics features were quantitatively extracted from H&E-stained whole-slide images. Predictive modeling incorporated machine learning approaches (DT, KNN, LASSO, NB, RF, SVM) with clinical variables. Model performance was evaluated through ROC analysis, calibration, and decision curve analysis. Biological interpretation leveraged TCGA transcriptomic data analyzed via GSEA and WGCNA.

[RESULTS] Tumor and peri‑tumor pathomics parameters showed some complementarity in the prediction of HCC recurrence. The combined LASSO-based model showed the best predictive efficacy, with AUCs of 0.850 and 0.807 in the training and validation sets, respectively. The integrated pathomics-clinical model achieved AUCs of 0.893 and 0.860 in training and validation sets. Bioinformatics analysis suggested that the pathomics was correlated with the tumor immune microenvironment, as verified by multiple immunofluorescence staining of the validation set.

[CONCLUSION] This study establishes a robust digital pathology framework that not only improves HCC recurrence prediction beyond conventional biomarkers but also provides mechanistic insights into tumor-immune crosstalk.