AI Boosts Survival Prediction in Liver Cancer Immunotherapy

Hepatocellular carcinoma (HCC), a major cause of cancer deaths, sees improved outcomes when artificial intelligence refines treatment strategies. A recent study shows how AI can better predict which patients will benefit most from combined immunotherapy and targeted therapies.

Improved Survival Rates

Combining immunotherapy and targeted therapy with local treatments like TACE or radiotherapy significantly extends survival for advanced HCC patients. Specifically, adding TACE increased median survival to 19.7 months, while radiotherapy boosted it to 22.3 months, compared to 12.8 months with immunotherapy and targeted therapy alone.

AI-Powered Prediction

Researchers analyzed 351 HCC patients, using five AI models to predict survival outcomes. The Random Survival Forest (RSF) model stood out, achieving a C-index of 0.731, indicating strong predictive accuracy.

Superior Model Performance

The RSF model demonstrated high accuracy in predicting survival at 6, 12, and 24 months, with AUC values of 0.844, 0.824, and 0.806, respectively. Decision curve analysis also confirmed the RSF model’s higher clinical benefit.

Refining Treatment Strategies

These findings suggest that AI can play a crucial role in personalizing HCC treatment. By accurately predicting patient responses, AI can help doctors select the most effective combination of therapies, ultimately improving survival rates and quality of life.