ArticleQuantitative imaging in medicine and surgery2024
Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.
Article in Quantitative imaging in medicine and surgery, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
1 citing paper in PubMed.
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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, 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.
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Registered trials
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.