Optimal treatment selection for hepatocellular carcinoma in the era of immunotherapy

Immunotherapy has revolutionized hepatocellular carcinoma (HCC) management, necessitating personalized strategies in current guidelines. Despite curative intent for early-stage HCC, recurrence rates remain high. While antiviral therapy mitigates risk, no adjuvant therapy is currently established for high-risk patients. Artificial intelligence (AI) models now offer promising tools for predicting post-resection recurrence.For intermediate-stage HCC, the 7-11 criteria and radiological patterns guide treatment selection. Combining transarterial chemoembolization (TACE) with tyrosine kinase inhibitors has already improved objective response rates (ORR) and progression-free survival (PFS) over TACE monotherapy. Furthermore, the Phase III EMERALD-1 and LEAP-012 trials demonstrated that TACE plus immunotherapy significantly extends PFS. The TALENTACE trial further investigates this synergy, evaluating the efficacy and safety of TACE combined with immune checkpoint inhibitors (ICIs) combination therapy to optimize outcomes in this population.Achieving a cancer-free state through curative conversion-utilizing liver-directed, systemic, or combination therapies-remains a primary clinical goal. While ICIs are the standard for advanced HCC, reliable biomarkers are still lacking. Emerging evidence suggests that the tumor microenvironment, genetic signatures, and the gut microbiota-metabolite axis are significantly associated with ICI outcomes. Integrating these complex biological features through machine learning holds the potential to identify robust biomarkers, ultimately refining immunotherapy selection and patient prognosis.