ArticleTranslational cancer research2021
Bibliometrics research on radiomics of lung cancer.
Article in Translational cancer research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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
6 citing papers in PubMed.
- Mapping the future: bibliometric analysis of omics research trends in non-small cell lung cancer.Discover oncology · 2025Article
- Analysis of Research Hotspots and Development Trends in the Diagnosis of Lung Diseases Using Low-Dose CT Based on BibliometricsCurrent medical imaging · 2025Review
- Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.Quantitative imaging in medicine and surgery · 2024Article
- Cancer research in the United Arab Emirates from birth to present: A bibliometric analysis.Heliyon · 2024Article
- Preoperative prediction of sonic hedgehog and group 4 molecular subtypes of pediatric medulloblastoma based on radiomics of multiparametric MRI combined with clinical parameters.Frontiers in neuroscience · 2023Article
- Knowledge mapping visualization of the pulmonary ground-glass opacity published in the web of science.Frontiers in oncology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLung cancer is currently the most commonly diagnosed malignant tumor worldwide. Exploring ways to improve the accuracy and timeliness of diagnosis has important clinical significance. Radiomics transforms images into high-dimensional data, and uses deep learning and artificial intelligence to improve the accuracy and efficiency of disease diagnosis. There is an increasing amount of research on radiomics in the diagnosis of lung cancer. This study analyzes the relevant literature in the Science Citation Index Expanded (SCI-E) database to understand the current research status and future development direction of lung cancer radiomics.
methodsThis study is based on the SCI-E database. The first search formula is topic = Lung cancer OR Lung neoplasms (#1), the second search formula is topic = Radiomics (#2), and the third search formula is #1 and #2, that is, literature that meets both the first and second search results. CiteSpace software was used to analyze lung cancer radiomics from the annual distribution of articles, countries, institutions, journals, and authors and keywords. HistCite software was used to visualize the citation chronology of the lung cancer radiomics literature, and Pajek software was used to analyze the main path of the citation chronology.
resultsThere were a total of 749 publications, of which most were original articles (529, 70.63%) and reviews (109, 14.55%). The citation frequency is 21,676 times, the h-index is 66, and the average number of citations per publication is 28.94. The research mainly comes from the United States of America, China and other countries. The research institutions are mainly medical centers such as Moffitt Cancer Center, Maastricht University and Harvard Medical School. The authors are also mainly from these institutions. The literature was published in many related journals, mainly imaging and oncology journals. Keyword analysis shows that in recent years, research has focused on deep learning and artificial intelligence.
conclusionsThe field of lung cancer radiomics is developing rapidly, and the main focuses of research are deep learning and artificial intelligence.
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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.