Global trends and hotspots of machine learning in the diagnosis of prostate cancer: a bibliometric analysis from 1997 to 2024
- 1Operating Room of Anesthesia Surgery Center West China Hospital/West China School of Nursing, Sichuan University, Sichuan, China
Abstract
PURPOSE: Prostate cancer (PCa) diagnosis has advanced with the integration of machine learning (ML). This study analyzes global trends in ML-based PCa diagnosis research using bibliometric methods.
METHODS: A systematic search was conducted in the Web of Science Core Collection database for articles published between 1997 and 2024. Bibliometric analysis was performed using VOSviewer (v1.6.20), CiteSpace (v6.3.R1), and R (v4.3.3).
RESULTS: The analysis included 1,045 articles, involving 6,704 authors from 4,762 institutions across 327 countries or regions. The number of publications increased over time, rising sharply from 2018. China published the highest number of articles (290), whereas the United States demonstrated the greatest research impact, leading in total citations (8,207) and international collaborations. The Berlin Institute of Health published the highest number of articles (120). Within this dataset, European Radiology had the highest H-index. Key authors included Stephan C, Jung K, and Cammann H. Keyword analysis identified “system,” “MRI,” and “guidelines” as prominent terms, with emerging trends focusing on “convolutional neural network,” “data system,” and “transfer learning.”
CONCLUSION: ML in PCa diagnosis has advanced substantially, transitioning from fundamental biomarker investigations to sophisticated deep learning applications centered on medical imaging. Future directions emphasize the development of accurate, generalizable ML models integrated into clinical workflows with a continued focus on convolutional neural networks and transfer learning.
CLINICAL SIGNIFICANCE: This study delineates the global research evolution of maching learning for prostate cancer diagnosis, offering clear clinical guidance for the translation and routine application of artificial intelligence-assisted diagnostic models.
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BibTeX
@article{shi2026global,
title = {{Global trends and hotspots of machine learning in the diagnosis of prostate cancer: a bibliometric analysis from 1997 to 2024}},
author = {Yixiao Shi},
journal = {Diagnostic and Interventional Radiology},
year = {2026},
month = nov,
doi = {10.4274/dir.2026.263844},
url = {https://doi.org/10.4274/dir.2026.263844},
note = {CC BY-NC 4.0},
}
RIS
TY - JOUR
TI - Global trends and hotspots of machine learning in the diagnosis of prostate cancer: a bibliometric analysis from 1997 to 2024
AU - Yixiao Shi
PY - 2026
DA - 2026/11/02
JO - Diagnostic and Interventional Radiology
DO - 10.4274/dir.2026.263844
UR - https://doi.org/10.4274/dir.2026.263844
AB - PURPOSE: Prostate cancer (PCa) diagnosis has advanced with the integration of machine learning (ML). This study analyzes global trends in ML-based PCa diagnosis research using bibliometric methods. METHODS: A systematic search was conducted in the Web of Science Core Collection database for articles published between 1997 and 2024. Bibliometric analysis was performed using VOSviewer (v1.6.20), CiteSpace (v6.3.R1), and R (v4.3.3). RESULTS: The analysis included 1,045 articles, involving 6,704 authors from 4,762 institutions across 327 countries or regions. The number of publications increased over time, rising sharply from 2018. China published the highest number of articles (290), whereas the United States demonstrated the greatest research impact, leading in total citations (8,207) and international collaborations. The Berlin Institute of Health published the highest number of articles (120). Within this dataset, European Radiology had the highest H-index. Key authors included Stephan C, Jung K, and Cammann H. Keyword analysis identified “system,” “MRI,” and “guidelines” as prominent terms, with emerging trends focusing on “convolutional neural network,” “data system,” and “transfer learning.” CONCLUSION: ML in PCa diagnosis has advanced substantially, transitioning from fundamental biomarker investigations to sophisticated deep learning applications centered on medical imaging. Future directions emphasize the development of accurate, generalizable ML models integrated into clinical workflows with a continued focus on convolutional neural networks and transfer learning. CLINICAL SIGNIFICANCE: This study delineates the global research evolution of maching learning for prostate cancer diagnosis, offering clear clinical guidance for the translation and routine application of artificial intelligence-assisted diagnostic models.
SN - 1305-3825
SN - 1305-3612
ER -