Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis

Background: Extensive research on the application of artificial intelligence (AI) and radiomics in lung cancer has been published in recent years; however, it is necessary to identify the current status, hotspots, and trends in the field. Thus, this study conducted a bibliometric analysis of relevant studies to investigate the application of AI and radiomics in lung cancer. Methods: Related publications were retrieved from the Web of Science Core Collection (WoSCC). CiteSpace generated the associated co-occurrence network maps in terms of institutions, authors, and keywords. Bibliometrix was used to perform a bibliometric analysis of relevant countries/regions and journals. In addition, the collected information was used to generate figures using R. Results: A total of 2,989 publications were included in this study, of which 2,804 (93.8%) were articles and 185 (6.2%) were reviews. In 2016, there was a rapid increase in the number of publications in this field. Most of the research originated from China (n=1,365, 45.7%). While Fudan University (n=109, 3.6%) attracted the greatest attention among all institutions. In terms of the authors, Gillies (28 publications, 0.9%) published the greatest number of articles. In terms of the journals, Frontiers in Oncology (n=177, 5.9%) and Radiology (5,152 citations) had the greatest number of publications and influence, respectively. The main keywords identified were "lung cancer", "deep learning", "classification", "computed tomography", and "features". Burst detection suggested that "texture", "image classification", and "false positive reduction" have recently appeared at the frontier of research. Conclusions: This study used bibliometric methods to analyze the relevant literature to discuss the current research hotspots and future trends in the application of AI and radiomics in lung cancer. This information may help relevant researchers to shape the direction of future studies, such as innovations in AI techniques standardized feature extraction, and extend understandings of epidermal growth factor receptor mutations.