Development and Validation of a Machine Learning-Based Individualized Model to Predict TKI Benefit in Postoperative Recurrent HCC.

Background: Postoperative recurrence is a major contributor to poor prognosis in hepatocellular carcinoma (HCC) after curative resection. Tyrosine kinase inhibitors (TKIs) are commonly used for recurrent HCC, yet substantial interpatient heterogeneity limits their universal benefit, and reliable tools to individualize post-recurrence therapy are lacking. Methods: We retrospectively analyzed 454 patients with recurrent HCC following curative resection. Overlap weighting based on propensity scores was applied to balance baseline characteristics between TKI-treated and non-TKI-treated groups and to assess the survival benefit of TKI therapy. Machine learning-based survival models were independently developed for each treatment cohort using multiple algorithms and internally validated. A counterfactual inference framework was implemented to estimate individualized survival outcomes under alternative treatment strategies and to identify the optimal therapy for each patient. Results: After overlap weighting, TKI therapy was associated with improved survival compared with non-TKI treatment, including longer recurrence-free survival (median, 24 vs. 12 months; HR = 0.355, Conclusion: TKI therapy provides significant survival benefit in recurrent HCC after curative resection. Our machine learning-based, counterfactual framework enables individualized estimation of treatment benefit, supporting personalized post-recurrence therapy and facilitating precision management in this high-risk population.