A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis

Kiseong Lee; Ji Woong Hwang; Hee Ju Sohn; Sanggyun Suh; Sun-Whe Kim

doi:10.3389/fonc.2023.1227991

A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis

Front Oncol. 2023 Aug 17:13:1227991. doi: 10.3389/fonc.2023.1227991. eCollection 2023.

Authors

Kiseong Lee¹, Ji Woong Hwang², Hee Ju Sohn², Sanggyun Suh², Sun-Whe Kim²

Affiliations

¹ Humanities Research Institute, Chung-Ang University, Seoul, Republic of Korea.
² Department of Surgery, Chung-Ang University Gwangmyeong Hospital, Chung-Ang University College of Medicine, Gwangmyeong, Republic of Korea.

Abstract

Introduction: Research on hepatocellular carcinoma (HCC) has grown significantly, and researchers cannot access the vast amount of literature. This study aimed to explore the research progress in studying HCC over the past 30 years using a machine learning-based bibliometric analysis and to suggest future research directions.

Methods: Comprehensive research was conducted between 1991 and 2020 in the public version of the PubMed database using the MeSH term "hepatocellular carcinoma." The complete records of the collected results were downloaded in Extensible Markup Language format, and the metadata of each publication, such as the publication year, the type of research, the corresponding author's country, the title, the abstract, and the MeSH terms, were analyzed. We adopted a latent Dirichlet allocation topic modeling method on the Python platform to analyze the research topics of the scientific publications.

Results: In the last 30 years, there has been significant and constant growth in the annual publications about HCC (annual percentage growth rate: 7.34%). Overall, 62,856 articles related to HCC from the past 30 years were searched and finally included in this study. Among the diagnosis-related terms, "Liver Cirrhosis" was the most studied. However, in the 2010s, "Biomarkers, Tumor" began to outpace "Liver Cirrhosis." Regarding the treatment-related MeSH terms, "Hepatectomy" was the most studied; however, recent studies related to "Antineoplastic Agents" showed a tendency to supersede hepatectomy. Regarding basic research, the study of "Cell Lines, Tumors,'' appeared after 2000 and has been the most studied among these terms.

Conclusion: This was the first machine learning-based bibliometric study to analyze more than 60,000 publications about HCC over the past 30 years. Despite significant efforts in analyzing the literature on basic research, its connection with the clinical field is still lacking. Therefore, more efforts are needed to convert and apply basic research results to clinical treatment. Additionally, it was found that microRNAs have potential as diagnostic and therapeutic targets for HCC.

Keywords: bibliometric analysis; hepatocellular carcinoma; latent Dirichlet allocation; machine learning; research trend.

Publication types

Review

Grants and funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korean government (Ministry of Science and ICT) (No.RS-2022-00165595).