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Studies published from 1991 to 2020 were retrieved from PubMed using MeSH “hepatocellular carcinoma,” and publication metadata were analyzed with latent Dirichlet allocation topic modeling. Results show sustained growth in annual publications, with diagnostic, treatment, and basic-research terms shifting over time. The review highlights limited translation from basic research to clinical treatment and notes microRNAs as potential targets.","TYPE Review  \nPUBLISHED 17 August 2023  \nDOI 10.3389/fonc.2023.1227991  \nOPEN ACCESS  \nEDITED BY  \nJohn Gibbs,  \nHackensack Meridian Health, United States  \nREVIEWED BY Saber A Amin,  \nUniversity of Nebraska Medical Center, United States  \nHaihong Zhu,  \nSchool of Medicine, Zhejiang University, China  \n*CORRESPONDENCE Ji Woong Hwang  \n [dattoree@gmail.com](dattoree@gmail.com)  \nRECEIVED 24 May 2023  \nACCEPTED 31 July 2023  \nPUBLISHED 17 August 2023  \nCITATION  \nLee K, Hwang JW, Sohn HJ, Suh S and Kim S-W (2023) A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based  \nbibliometric analysis.  \nFront. Oncol. 13:1227991 .  \ndoi: 10.3389/fonc.2023.1227991  \nCOPYRIGHT  \n© 2023 Lee, Hwang, Sohn, Suh and Kim.  \nThis is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA systematic review of progress on hepatocellular carcinoma research over the past 30 years:  \na machine-learning-based bibliometric analysis  \nKiseong Lee 1, Ji Woong Hwang 2*, Hee Ju Sohn 2, Sanggyun Suh 2 and Sun-Whe Kim 2  \n1 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  \nIntroduction: 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.  \nMethods: 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.  \nResults: 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.  \nConclusion: 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.  \nFrontiers in Oncology 01 [frontiersin.org](frontiersin.org)  \nAdditionally, it was found that microRNAs have potential as diagnostic and therapeutic targets for HCC.  \nKEYWORDS  \nhepatocellular carcinoma, bibliometric analysis, machine learning, latent Dirichlet allocation, res","cbCaid9gUvAXkZWG","https://ap.wps.com/l/cbCaid9gUvAXkZWG","pdf",2264077,1,"English","en",105,"# Introduction\n## Background and rationale\n# Methods\n## Data source and search strategy\n## Topic modeling approach\n# Results\n## Publication growth and term shifts\n## Diagnosis, treatment, and basic research trends\n# Conclusion\n## Translation gap and future needs","[{\"question\":\"What was the objective of the machine-learning-based bibliometric study?\",\"answer\":\"To analyze research progress on hepatocellular carcinoma over the past 30 years and suggest future research directions.\"},{\"question\":\"How were publications collected and what modeling method was used?\",\"answer\":\"Publications from 1991 to 2020 were retrieved from PubMed using MeSH “hepatocellular carcinoma,” and latent Dirichlet allocation topic modeling was applied using Python to analyze research topics.\"},{\"question\":\"What major trends did the study find in research keywords over time?\",\"answer\":\"Annual publications showed constant growth, diagnostic terms such as “Liver Cirrhosis” were overtaken by “Biomarkers, Tumor” in the 2010s, and treatment-related interests shifted toward “Antineoplastic Agents.”\"},{\"question\":\"What limitation did the authors identify and what future work is suggested?\",\"answer\":\"Despite strong analysis efforts on basic research, its connection with clinical practice was still lacking, indicating a need to convert and apply basic findings to clinical treatment.\"}]","A systematic review of progress on hepatocellular carcinoma research over the past 30 years - 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