Decade-long landscape of transrectal ultrasound (TRUS) in prostate cancer research: trends, collaborations, and emerging frontiers
Bibliographic record
Abstract
OBJECTIVE: To systematically reveal the spatiotemporal distribution, collaboration networks, and thematic evolution of global transrectal ultrasound (TRUS) research in prostate cancer from 2015 to 2024 using bibliometric analysis and visualization. METHODS: A total of 12,894 relevant articles from the Web of Science database were analyzed using VOSviewer and CiteSpace for co-occurrence networks, burst detection, and density visualization, combined with social network analysis (SNA) and kernel density estimation (KDE) to decode country/institution collaboration patterns and geographical agglomeration. RESULTS: Annual publications peaked at 1800 in 2019, with a secondary surge in 2023 (1563 articles) driven by AI applications (e.g., AI-assisted biopsy). Mean citations per article reached 7.8 in 2020, coinciding with the release of GLOBOCAN 2020 and the rise of teleultrasound research. The United States (5333 articles) and Canada formed the North American core cluster (edge weight 5333 × 1099), while the UK (1287 articles) and Germany served as secondary hubs in Europe. Asian countries showed scattered distributions, though South Korea (2015-2017 burst strength 7.64, elastography) and Australia (2019-2022 burst strength 8.33, focal ablation) emerged as regional technical frontiers. The University of Toronto (242 articles) led TRUS-targeted biopsy research, while the University of Michigan became a rising affiliation due to TRUS-AI integration (post-2020 annual publications > 50). The high-impact journal J Clin Oncol (5783 citations) focused on fusion biopsy, with annual articles increasing from 42 to 117.Keywords shifted from "systematic biopsy" (2015-2017) and "radiotherapy planning" (2018-2020) to "AI-assisted diagnosis" (2021-2024 burst strength 20.6) and "teleultrasound" (COVID-19-related studies, annual growth 19%), reflecting technology-driven clinical transformation. CONCLUSION: TRUS research exhibits significant regional inequality and technological iteration, with AI integration and multimodal fusion (e.g., MRI-TRUS fusion) as future priorities. Strengthening cross-regional collaboration is recommended to promote technological equity, particularly for low-cost TRUS innovations in resource-constrained regions.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.037 | 0.062 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".