Bibliometrics research on radiomics of lung cancer

BACKGROUND: Lung cancer is currently the most commonly diagnosed malignant tumor worldwide. Exploring ways to improve the accuracy and timeliness of diagnosis has important clinical significance. Radiomics transforms images into high-dimensional data, and uses deep learning and artificial intelligence to improve the accuracy and efficiency of disease diagnosis. There is an increasing amount of research on radiomics in the diagnosis of lung cancer. This study analyzes the relevant literature in the Science Citation Index Expanded (SCI-E) database to understand the current research status and future development direction of lung cancer radiomics. METHODS: This study is based on the SCI-E database. The first search formula is topic = Lung cancer OR Lung neoplasms (#1), the second search formula is topic = Radiomics (#2), and the third search formula is #1 and #2, that is, literature that meets both the first and second search results. CiteSpace software was used to analyze lung cancer radiomics from the annual distribution of articles, countries, institutions, journals, and authors and keywords. HistCite software was used to visualize the citation chronology of the lung cancer radiomics literature, and Pajek software was used to analyze the main path of the citation chronology. RESULTS: There were a total of 749 publications, of which most were original articles (529, 70.63%) and reviews (109, 14.55%). The citation frequency is 21,676 times, the h-index is 66, and the average number of citations per publication is 28.94. The research mainly comes from the United States of America, China and other countries. The research institutions are mainly medical centers such as Moffitt Cancer Center, Maastricht University and Harvard Medical School. The authors are also mainly from these institutions. The literature was published in many related journals, mainly imaging and oncology journals. Keyword analysis shows that in recent years, research has focused on deep learning and artificial intelligence. CONCLUSIONS: The field of lung cancer radiomics is developing rapidly, and the main focuses of research are deep learning and artificial intelligence.