Quantitative analysis of artificial intelligence on liver cancer: A bibliometric analysis - Xiong, Ming; Xu, Yaona; Zhao, Yang; He, Si; Zhu, Qihan; Wu, Yi; Hu, Xiaofei; Liu, Li (Frontiers in Oncology, 2023)
Objective: Liver cancer is a common malignant tumor with extremely aggressive behavior, low surgical resection and resectability rates. The development of big data and artificial intelligence (AI) technologies have driven exponential advancement of AI in medical fields, including prevention, diagnosis, staging, treatment and response assessment of liver cancer. In this study, the bibliometric method was adopted to analyze the status of studies on AI in liver cancer and provide a reference for future work. Methods: In this study, the Web of Science Core Collection (WoSCC) database was used to perform systematic searches using keywords. Microsoft Excel 2019 was used to collect the targeted variables from retrieved articles. VOSviewer, Citespace and SRplot were applied to perform a bibliometric visualization analysis, including the analysis of trends of publications, citations, countries/regions, institutions, authors and co-cited authors, journals, cited references and co-citation references, and keywords. Results: 1724 papers were collected in this study, including 1547 original articles and 177 reviews. The study of AI in liver cancer mostly began from 2003 and has developed rapidly from 2017. China has the largest number of publications, and the United States has the highest H-index and total citation counts. The top three most productive institutions are the League Of European Research Universities, Sun Yat Sen University, and Zhejiang University. Jasjit S. Suri and Frontiers in Oncology are the most published author and journal, respectively. Keyword analysis showed that in addition to the research on liver cancer, research on liver cirrhosis, fatty liver disease, and liver fibrosis were common. Computed tomography was the most used data type, followed by ultrasound and magnetic resonance imaging. The diagnosis and differential diagnosis of liver cancer are currently the most widely adopted research goals, and comprehensive analyses of multi-type data and postoperative analysis of patients with advanced liver cancer are rare. The use of convolutional neural networks is the main technical method used in studies of AI on liver cancer. Conclusion: Current studies focus on the diagnosis of liver cancer. However, multi-type data fusion analysis and development of multimodal treatment plans for liver cancer would become the major trend of future research.