ArticleQuantitative imaging in medicine and surgery2025
Exploring the evolving landscape of radiomics in lung cancer: a comprehensive bibliometric analysis [2008-2024].
Article in Quantitative imaging in medicine and surgery, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Authors and funding
5 authors.
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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: A total of 1,324 articles were analyzed. China led in productivity with 622 publications, whereas the University of Texas System was the top contributing institution with 163 publications. 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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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.