Algorithms on the rise: a machine learning–driven survey of prostate cancer literature
Bibliographic record
Abstract
Introduction: Machine learning (ML) has shown significant potential in improving prostate cancer (PCa) diagnosis, prognosis, and treatment planning. Despite rapid advancements, a comprehensive quantitative synthesis of global research trends and the knowledge structure of ML applications in PCa remains lacking. This study aimed to systematically map the evolution, research hotspots, and collaborative landscape of ML-PCa research. Methods: A systematic bibliometric review was performed on English-language articles and reviews published between January 2005 and December 2024. Publications were retrieved from the Web of Science (WOS) and Scopus databases. Analytical tools including CiteSpace, VOSviewer, and the R-bibliometrix package were employed to assess publication growth trends, country and institutional contributions, collaboration networks, author productivity, journal outlets, and keyword co-occurrence patterns. Results: A total of 2,632 publications were identified. Annual output increased from fewer than 20 papers during 2005-2014 to 661 in 2024, with 82% of all studies published since 2021. Emerging frontiers included deep learning, radiomics, and multimodal data fusion. China (649 publications) and the United States (492 publications) led in research volume, while Germany demonstrated the highest proportion of multinational collaboration (39.29%). Leading institutions by output were the Chinese Academy of Sciences, the University of British Columbia, and Shanghai Jiao Tong University. In terms of citation impact, the University of Toronto, Case Western Reserve University, and the University of Pennsylvania ranked highest. The journals Cancers, Frontiers in Oncology, and Scientific Reports published the most ML-PCa studies, highlighting the cross-disciplinary nature of the field. Madabhushi Anant emerged as the most central author hub in global collaboration networks. Discussion: ML applications in PCa research have experienced exponential growth, with methodological innovations driving interest in deep learning and radiomics. However, a persistent translational gap exists between algorithmic development and clinical implementation. Future directions should focus on fostering interdisciplinary collaboration, conducting prospective multicenter validation studies, and aligning with regulatory standards to accelerate the integration of ML models into clinical PCa workflows.
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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 | 0.018 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.043 | 0.060 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".