Global research status and hotspots of radiotherapy for prostate cancer: a bibliometric analysis based on Web of Science from 2010-2022
Bibliographic record
Abstract
Background Radiotherapy (RT) is one of the important treatments for various cancer types and its application to prostate cancer (PCa) has also gradually gained increasing attention. However, there is a lack of comprehensive and objective studies on the overall status of research on RT for PCa. This article aims to summarize and quantify the dynamic trends of RT in PCa by using bibliometrics. Methods Studies on RT for PCa were screened from the Web of Science Core Collection (WoSCC) database between 1 January 2010 and 21 November 2022 to collate and quantify information characteristics by analyzing parameters including annual publications, countries/regions, institutions and authors with the aid of the bibliometric software CiteSpace and VOSviewer. In addition, research trends and hotspots were explored by analyzing keywords and co-cited references. Results A total of 21338 documents were retrieved. The United States of America (USA) ranked first and maintained the leading position among all countries in the number of publications (8489) and total citations (266342). The University of Toronto was the most active institution in total publications (n=587). Paul L Nguyen enjoyed the most publications (n=179), and Michael J Zelefsky enjoyed the most co-citations (n=3376). INTERNATIONAL JOURNAL OF RADIATION ONCOLOGY BIOLOGY PHYSICS published the most papers (n=1026), and was the most frequently co-cited journal (n=78550). The largest and closest cluster in the reference cluster analysis was “oligorecurrent prostate cancer”. The timeline view of keywords reveals that cluster “biochemical recurrence(BCR)” is ongoing. Moreover, keywords burstness analysis showed that “radiation dosimetry”, “dose rate brachytherapy(BT)”, “salvage radiotherapy”, “stereotactic body radiotherapy(SBRT)”, “guideline”, and “multicenter” were the terms with great bursts in the past a few years. Conclusion The application of RT targeting oligometastatic prostate cancer(OMPC) has garnered considerable attention among researchers. SBRT and BT have become hot topics in the field. Additionally, the BCR of PCa has long been a critical issue requiring extensive research and resolution, and salvage radiotherapy has currently emerged as a closely related research focus. Related large-scale multicenter studies have been conducted over the past few years, providing valuable insights. More high-quality research is expected to be employed to guide clinical decision-making.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.115 | 0.173 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".