Abstract
BACKGROUND: Immune checkpoint inhibitors (ICIs) have changed the treatment landscape of hepatocellular carcinoma (HCC), especially those with unresectable advanced stages. The field has progressed rapidly, and the research hotspots have significantly changed compared to previous years. The study aims to comprehensively review and analyze the development history, knowledge structure, current research focus, and emerging trends in ICIs for HCC. MATERIALS AND METHODS: Reviews and articles published in English from The Web of Science Core Collection (WoSCC) database from 2014 to 2024 were systemically retrieved. Citespace, VOSviewer, and Bibliometrix R package were used for further bibliometric analysis and visualization for countries, institutions, authors, references, and keywords. RESULTS: 2,941 records were included for analysis. The literature on ICIs for HCC has continued to grow steadily over the past decade. Three major research centers have emerged: North America, Europe, and East Asia. The Chinese institution has the highest publication volume, but Kudo Masatoshi from Japan has the highest number of publications. At the same time, Richard S. Finn from the United States leads in citations and co-citations. The most prolific journal is "Cancers". The clustering and Timeline view of critical literature and keywords indicated that research on ICIs for HCC is rapidly advancing toward a more evidence-based, personalized, and multimodal approach. Immune evasion mechanisms, predictive biomarkers, and high-quality clinical trials focusing on Novel combination, conversion, and perioperative therapies, including ICIs, are emerging hotspots. CONCLUSION: This study highlights the groundbreaking advancements of ICIs in treating HCC and shows a trend rapidly advancing towards a more evidence-based, personalized, and multimodal approach. The study updated the current understanding of ICIs in hepatocellular carcinoma and identified vital future directions for research, such as the exploration of mechanisms of immune evasion, developing predictive biomarkers, and combining therapy strategies.