Bibliometric analysis of application of radiomics and artificial intelligence integration in personalized treatment of hepatocellular carcinoma

Recent advancements in personalized medicine have highlighted the importance of customized strategies in managing hepatocellular carcinoma (HCC), especially through the use of radiomics and artificial intelligence (AI) in treatment planning. While many studies have focused on how radiomics and AI can aid in prognostic evaluations, there is still a significant lack of thorough assessments regarding research trends and gaps in the existing literature. This study employed bibliometric analysis to explore the landscape of personalized treatment research in HCC, specifically concentrating on radiomics and AI techniques. The research utilized the Web of Science Core Collection (WoSCC) database to collect relevant literature, and various tools such as VOSviewer, CiteSpace, and the bibliometrix R package were used to visualize bibliometric results, along with additional visualizations from an online platform. Over a period of 14 years, a total of 615 publications were identified. The majority of these publications originate from China and the United States, with Sun Yat-sen University leading in output. The journals making the most significant contributions to this field were Frontiers in Oncology and European Radiology. Notably, keywords showing strong citation bursts in recent years included biomarkers, accuracy, and body radiation therapy. This bibliometric analysis offers a crucial framework for understanding the foundational knowledge in this area. The field of personalized treatment for HCC, particularly with an emphasis on radiomics and AI, is currently undergoing rapid growth, underscoring its ongoing importance for future research initiatives.