Comprehensive Bibliometric Analysis of Prediction Models for HCC: Current Trends and Future Prospects

Area of Science:

• Oncology
• Bibliometrics
• Predictive Modeling

Background:

• Hepatocellular carcinoma (HCC) is a major global health concern with increasing incidence and mortality.
• Accurate prediction of liver cancer is crucial for enhancing patient prognosis and treatment strategies.

Purpose of the Study:

• To conduct a bibliometric analysis of liver cancer prediction research.
• To identify current research hotspots, key contributors, and emerging trends in the field.

Main Methods:

• A comprehensive literature search was performed on the Web of Science database.
• Bibliometric analysis was conducted using CiteSpace software to analyze publication data, including authors, countries, institutions, and keywords.

Main Results:

• The study identified 1092 articles from 114 countries, with China and the USA as leading contributors.
• Research has evolved from focusing on risk factors and staging to emphasizing DNA methylation, immune microenvironments, and tumor metastasis.
• Key journals include Hepatology and Journal of Hepatology, with significant contributions from authors like Kim Seung Up.

Conclusions:

• This bibliometric analysis offers a comprehensive overview of the landscape of liver cancer prediction research.
• The findings highlight the growing importance of multi-omics data integration and artificial intelligence (AI) in developing advanced predictive models for liver cancer.