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

IntroductionResearch 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.MethodsComprehensive 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.ResultsIn 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.ConclusionThis 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

Impact of tumor size on hepatectomy outcomes in hepatocellular carcinoma: a nationwide propensity score matching analysis

Purpose: The aim of this study was to compare surgical outcomes after liver resection for hepatocellular carcinoma (HCC) according to tumor size using a large, nationwide cancer registry-based cohort and propensity score matching. Methods: From 2008 to 2015, a total of 12,139 patients were diagnosed with liver cancer and registered in the Korean Primary Liver Cancer Registry. Patients without distant metastasis who underwent hepatectomy as a primary treatment were selected. We performed 1:1 propensity score matching between the small (<5 cm), large (>= 5 cm and <10 cm), and huge (>= 10 cm) groups. Results: Overall, 265 patients in the small and large groups were compared, and 64 patients each in the large and huge groups were compared. The overall and progression-free survival rates were significantly lower in the large group than in the small group (P < 0.001 and P < 0.001, respectively). Overall survival tended to be poorer in the huge group than in the large group (P = 0.051). The progression-free survival rate was significantly lower in the huge group than in the large group (P = 0.002). Conclusion: Although primary liver resection can be considered even in patients with huge HCC, greater caution with careful screening for recurrence is needed.N

DataSheet_2_A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis.xlsx

IntroductionResearch 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.MethodsComprehensive 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.ResultsIn 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.ConclusionThis 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.</p

DataSheet_1_A systematic review of progress on hepatocellular carcinoma research over the past 30 years: a machine-learning-based bibliometric analysis.docx

IntroductionResearch 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.MethodsComprehensive 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.ResultsIn 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.ConclusionThis 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.</p

Changes in Indices of Steatosis and Fibrosis in Liver Grafts of Living Donors After Weight Reduction

BackgroundA short-term weight reduction program for potential living donors was introduced to reduce the extent of hepatic steatosis prior to liver transplantation. We aimed to investigate changes in non-invasive hepatic steatosis and fibrosis indices among those who completed the program. MethodsAmong 1,950 potential living liver donors between January 2011 and May 2019, 160 living donors joined the weight reduction program. The prospectively collected clinical data of these potential liver donors were analyzed retrospectively. Hepatic steatosis and fibrosis scores were determined using the fatty liver index (FLI), hepatic steatosis index (HSI), and NAFLD fibrosis score (NFS) and compared to MR spectroscopy (MRS) fat fraction results before and after weight reduction. ResultsThirty-nine potential living donors who had undergone MRS both before and after weight reduction were included in the analysis. Their body weight decreased from 78.02 +/- 10.89 kg to 72.36 +/- 10.38 kg over a mean of 71.74 +/- 58.11 days. FLI, HSI, and MRS values decreased significantly from 41.52 +/- 19.05 to 24.53 +/- 15.93, 39.64 +/- 3.74 to 35.06 +/- 3.82, and 12.20 +/- 4.05 to 6.24 +/- 3.36, respectively. No significant decreases in NFS were observed. There was a significant correlation between the extent of HSI change and the extent of MRS change (R-2 value = 0.69, P &lt; 0.001), although there was no correlation between MRS and FLI. ConclusionThe weight reduction program significantly improved non-invasive indices of hepatic steatosis over a short period. HSI may allow for prediction of simple decreases in hepatic steatosis.N

Long-term survival outcome beyond the first year of for pediatric acute liver failure: A large-volume living donor liver transplantation single center study

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