Deep learning-assisted tumor radiomic dynamics on MRI predict pathological complete response in HCC undergoing immune-based therapy followed by hepatectomy

BACKGROUND AND AIMS: Pathological complete response (pCR) following conversion therapy for initially unresectable hepatocellular carcinoma (uHCC) remains challenging to predict preoperatively. This study developed and validated a model integrating clinicopathological and radiomic features of the tumor to predict pCR. METHODS: In this multicenter retrospective study, temporal radiomics features were extracted from baseline, post-treatment, and delta (change) MRIs. Serum AFP response was calculated as log₁₀(preoperative AFP)/log₁₀(baseline AFP). Univariate analysis, collinearity assessment, LASSO, and random forest were employed to perform feature selection. Fourteen machine learning models were benchmarked, with performance evaluated by using comprehensive metrics AUC, NPV, PPV, sensitivity, specificity, calibration, and decision curve analysis. RESULTS: The model was developed and validated in a training (n=78), an internal test (n=32), and an independent validation cohort (n=44). The delta radiomic model significantly outperformed both baseline (test AUC: 0.835 vs. 0.483, p