Research hotspots and trends in lung cancer STAS: a bibliometric and visualization analysis - Peng, Xiuhua; Bian, Hupo; Zhao, Hongxing; Jia, Dan; Li, Mei; Li, Wenhui; Xu, Pengliang (Frontiers in Oncology, 2025)
PurposeThis study employed the R software bibliometrix and the visualization tools CiteSpace and VOSviewer to conduct a bibliometric analysis of literature on lung cancer spread through air spaces (STAS) published since 2015.MethodsOn September 1, 2024, a computer-based search was performed in the Web of Science (WOS) Core Collection dataset for literature on lung cancer STAS published between January 1, 2015, and August 31, 2024. VOSviewer was used to visually analyze countries, institutions, authors, co-cited authors, and keywords, while CiteSpace was utilized to analyze institutional centrality, references, keyword bursts, and co-citation literature. Descriptive analysis tables were created using Excel 2021.ResultsA total of 243 articles were included from the WOS, with a significant increase in annual publications observed since 2018. China, Kadota K, and Fudan University were leading countries, authors, and institutions by publication volume. The top three authors by co-citation count were Kadota K, Chen C, and Adusumilli PS. The journal with the highest publication volume was Lung Cancer, with the most influential journal among the top 10 being the Journal of Thoracic Oncology. The most frequently cited reference was “Lobectomy Is Associated with Better Outcomes than Sublobar Resection in Spread through Air Spaces (STAS)-Positive T1 Lung Adenocarcinoma: A Propensity Score-Matched Analysis.” Keyword clustering categorized the research into four main areas: pathological studies of lung cancer STAS, biological mechanisms, prognostic assessment, and imaging analysis. Current research hotspots include deep learning, lung squamous cell carcinoma, and air spaces STAS.ConclusionThe current research on lung cancer STAS primarily focuses on pathological studies, biological mechanisms, prognostic assessments, and preoperative imaging model predictions. This study’s findings provide new insights and directions for future research in this area.Systematic review registrationhttps://www.crd.york.ac.uk/prospero/#myprospero, identifier 589442.