Artificial intelligence-based evaluation of prognostic benefits from immunotherapy plus targeted therapy with or...

Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, and the prognosis of advanced disease is still poor. Immunotherapy plus targeted therapy has reshaped systemic treatment; however, the overall efficacy is limited. Increasing evidence suggests that combining systemic therapy with locoregional modalities such as transarterial chemoembolization (TACE) or radiotherapy (RT) may improve survival. Artificial intelligence (AI) offers the potential to refine prognostic prediction and optimize patient selection.

We retrospectively analyzed 351 patients with unresectable HCC, classified into three groups: immunotherapy plus targeted therapy (P+T, n = 89), P+T combined with TACE (n = 154), and P+T combined with RT (n = 108). Univariable Cox regression identified prognostic factors, which were incorporated into five AI models. Model performance was evaluated using the C-index, Brier score, time-dependent receiver operating characteristics (ROC), decision curve analysis (DCA), and calibration.

The median overall survival (mOS) was 12.8 months in the P+T group, 19.7 months in the TACE group (p = 0.011), and 22.3 months in the RT group (p = 0.030). Among the five AI models, random survival forest (RSF) showed the best performance (C-index = 0.731) with favorable calibration. In the time-dependent ROC analysis, the RSF model achieved area under the curve (AUC) values of 0.844, 0.824, and 0.806 for the prediction of 6-, 12-, and 24-month survival, respectively. DCA indicated a higher net clinical benefit with the RSF model, and the calibration plots showed good agreement between the predicted and the observed survival.

Immunotherapy plus targeted therapy combined with TACE or RT significantly improved survival in advanced HCC compared with systemic therapy alone. RSF provided superior predictive performance and identified critical prognostic variables, supporting AI-assisted approaches as valuable tools for individualized risk stratification and treatment optimization in advanced HCC.