Exploring the evolving landscape of radiomics in lung cancer: a comprehensive bibliometric analysis [2008–2024]

Abstract

Background: Radiomics in lung cancer represents a transformative advancement in oncology, utilizing high-dimensional data from medical imaging to enhance diagnosis, prognosis, and treatment prediction. In this study, we conducted a bibliometric analysis to explore the research landscape and frontier trends of radiomics in lung cancer. Methods: A bibliometric analysis was conducted using the Web of Science Core Collection (WoSCC) to gather literature about "radiomics in lung cancer" from 2008 to 2024. Bibliometric analysis and data visualization were conducted using VOSviewer, CiteSpace, and the R package "Bibliometrix". Results: emerged as one of the leading journals with 58 articles and an h-index of 26. Aerts Hugo J. W. L., with 32 publications and 16,773 citations, was the most influential author. Keyword clustering analysis categorized the research into four themes: Cluster 1 focused on diagnostic imaging, Cluster 2 explored clinical outcomes and treatment strategies, Cluster 3 addressed tumor biology, and Cluster 4 highlighted tumor heterogeneity with prediction models. Keyword burst analysis emphasized terms such as "CT images", "immunotherapy", "prognosis", and "prediction model". Conclusions: This bibliometric study highlights radiomics' growing impact on lung cancer research, emphasizing diagnostic imaging, and personalized medicine. Future research should center on standardizing methodologies and prediction models, and integrating multi-modal data to enhance diagnostics, treatment planning, and personalized care.

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