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人工智能在肝癌中的研究趋势：文献计量学分析
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Abstract:
目的：通过文献计量学分析，对人工智能在肝癌中的相关研究进行了评估，探讨了2002~2021年的研究热点和现状。方法：2002~2021年期间，从Web of Science核心馆藏(WoSCC)中检索肝癌研究中人工智能相关的出版物。运用VOSviewer及Citespace进行文献计量分析，探讨了2002~2021年的研究热点和现状。结果：从WoSCC数据库中共检索到该领域的773篇出版物。2016年后，出版物数量快速增长。中国和中山大学分别是最具影响力的国家和机构。共调查关键词3339个，其中49个关键词出现次数超过20次。关键词被分为17个簇：#0表达，#1肝细胞癌，#2识别，#3智能神经网络，#4生物标志物发现，#5晚期肝细胞癌，#6肝细胞脂肪变，#7深度学习，#8结构分析，#9计算机辅助诊断，#10机器学习，#11协同作用，#12肝硬化，#13丙型肝炎，#14体外，#15肝质量，#16肝脏。结论：与AI相关的肝癌研究正处于发展阶段。目前，人工智能在肝癌的生物学、影像学、治疗及风险评估方面得到了广泛的研究。这项文献计量学分析展示了该领域研究的现状，并帮助研究人员确定了新的研究方向。
Purpose: We evaluated the related research on artificial intelligence in liver cancer (LC) through bibliometrics analysis and explored the research hotspots and current status from 2002 to 2021. Methods: Publications related to AI in LC were retrieved from the Web of Science Core Collection (WoSCC) during 2002~2021. VOSviewer and Citespace were used to bibliometrics analysis. Results: A total of 773 publications in the field were retrieved from the WoSCC database. After 2016, the number of publications increased rapidly. China and Sun Yat-sen University are the most influential countries and institutions, respectively. A total of 3339 keywords were investigated, among which 49 keywords appeared more than 20 times. Keywords are grouped into 17 clusters: #0 expression, #1 hepatocellular carcinoma, #2 recognition, #3 intelligent neural network, #4 Biomarker Discov-ery, #5 advanced hepatocellular carcinoma, #6 hepatocellular steatosis, #7 deep learning, #8 Structural analysis, #9 computer-aided diagnosis, #10 machine learning, #11 synergism, #12 cir-rhosis, #13 hepatitis C, #14 in vitro, #15 liver mass, #16 liver. Conclusion: Ai-related liver cancer research is in the developing stage. At present, artificial intelligence has been extensively studied in the biology, imaging, treatment and risk assessment of liver cancer. This bibliometric analysis shows the current state of research in this field and helps researchers identify new research direc-tions.
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