ReviewClinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico2025
Advancements and future trends in machine learning for lung cancer: a comprehensive bibliometric analysis.
Review in Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico, 2025. 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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
- Knowledge mapping and research trends of chimeric antigen receptor T-cell immunotherapy in breast cancer: A bibliometric and visual analytics study.Human vaccines & immunotherapeutics · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundIn recent years, significant progress has been made in lung cancer screening, diagnosis, and treatment with the continuous development of machine learning (ML).
methodsTo systematically explore the evolution and core driving factors of ML in lung cancer research since 2004, we conducted a comprehensive bibliometric analysis of 1,826 academic papers retrieved from the Web of Science Core Collection.
resultsThis study reveals that the USA is at the forefront of applying ML in lung cancer research. The institutional analysis indicates that Harvard University plays a key role as a leading institution in this field. In the author co-occurrence network analysis, Madabhushi Anant stood out as a significant contributor to the application of ML in lung cancer research. Additionally, journal co-occurrence analysis shows that the SCI REP-UK published the highest volume of papers in this area. It is worth noting that several prestigious medical journals, including NEW ENGL J MED, NATURE, and CA-CANCER J CLIN, have shown significant interest in this research field. The burst citation analysis of keywords and references indicates that research hotspots have evolved from early attention to "breast cancer" and "radiotherapy" (2004-2012) to a focus on "computer-aided diagnosis" (2013-2017). Since 2018, "texture analysis", "computer-aided detection", "survival prediction", and "radiomics" have emerged as new research trends.
conclusionAs ML continues to be applied more extensively and deeply in lung cancer, "computer-aided detection," "survival prediction," and "radiomics" are emerging as vital areas, deserving more attention from researchers.
Indexed as
Identifiers
40465135What OpenQuestion holds
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.