Hotspots and Research Trends in Machine Learning for Prostate Cancer - A Bibliometric Analysis and Visualization - 1997-2025

Aug 2, 2026

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A bibliometric analysis investigates how prostate cancer (PCa) research evolved and how machine learning (ML) and artificial intelligence (AI) influence diagnosis, classification, and treatment. Publications from 1997 to 2025 are analyzed using Web of Science Core Collection, with VOSviewer and Bibliometrix for bibliometric mapping. Results summarize publication volume, most cited studies, author and country collaborations, thematic shifts, and citation networks. Findings show a clear rise over recent years, with growth in ML applications and a need for prospective multicenter validation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Abstract

Introduction

Materials and Methods

Results

Conclusion

Original Article DOI: 10.4274/uob.galenos.2025.2025.5.2 Bull Urooncol 2025;24(3):57-69 Hotspots and Research Trends in Machine Learning for Prostate Cancer: A Bibliometric Analysis and Visualization (1997-2025) Tunahan Ateş1, Nezih Tamkaç2, İbrahim Halil Şükür3, Fesih Ok3, İsmail Önder Yılmaz4, Mutlu Değer4, Volkan İzol4 1 Defne State Hospital, Clinic of Urology, Hatay, Türkiye 2Hatay Mustafa Kemal University Faculty of Medicine, Department of Urology, Hatay, Türkiye 3University of Health Sciences Türkiye, Adana Training and Research Hospital, Department of Urology, Adana, Türkiye 4Çukurova University Faculty of Medicine, Department of Urology, Adana, Türkiye Abstract Objective: This bibliometric analysis examines the evolution of prostate cancer (PCa) research and evaluates the impact of machine learning and artificial intelligence (AI) on its diagnosis, classification, and treatment. Materials and Methods: Articles published between 1997 and 2025 were analysed using the Web of Science Core Collection database. VOSviewer and Bibliometrix software was utilized for bibliometric analysis. Terms such as “PCa”,“machine learning (ML)”,“deep learning” and “AI” were included in the search strategy. The number of publications, the most cited studies, author collaborations and country collaborations, thematic trends, and citation networks were visualised. Results: A total of 3,277 articles were analysed. The in augural article was published in 1997. Over the past five years, there has been a significant increase in the number of articles published. The United States and China are the countries with the highest number of publications, and the most influential authors and institutions are concentrated in these countries. A marked upward trend has been observed in ML applications for PCa diagnosis, risk stratification, and treatment planning. Conclusion: The use of AI and ML in PCa research has grown significantly over the last 20 years. However, most of the existing models have been tested with retrospective data, and more multicenter and prospective studies are needed for clinical applications. Comprehensive clinical validation is essential before AI-based systems can be reliably implemented. Keywords: Prostate cancer, machine learning, artificial intelligence, bibliometric analysis, scientific trends Introduction Prostate cancer (PCa) ranks as the second most prevalent cancer among men globally and constitutes a substantial proportion of cancer-related mortality (1) . This disease is particularly common in older men and may progress aggressively, with a high risk of metastasis if not detected early (1) . Currently, the standard diagnostic methods for PCa include the prostatespecific antigen (PSA) test, multiparametric magnetic resonance imaging (mpMRI), and biopsy (2) . Nevertheless, conventional diagnostic methods are not consistently definitive, and instances of false negatives or false positive results may occur (3) . In this context, machine learning (ML) techniques offer innovative and promising approaches for the diagnosis and treatment of PCa, encompassing areas such as medical imaging analysis and biomarker discovery (3) . ML is a subset of artificial intelligence (AI) that enhances clinical decision-making support through the analysis of large-scale datasets. In recent years, various ML methodologies, including Cite this article as: Ateş T, Tamkaç N, Şükür İH, et al. Hotspots and research trends in machine learning for prostate cancer: a bibliometric analysis and visualization (1997-2025) . Bull Urooncol. 2025;24(3):57-69 . Address for Correspondence: Tunahan Ateş, MD, Defne State Hospital, Clinic of Urology, Hatay, Türkiye E-mail: drtunahanates0101@gmail.com ORCID: [orcid.org/0000-0001-9087-290X](orcid.org/0000-0001-9087-290X)[ ](orcid.org/0000-0001-9087-290X)[Received:](Received: 15.05.2025 Accepted: 03.06.2025 Publication Date: 24.09.2025)[ 15.05.2025](Received: 15.05.2025 Acce

FAQs

What is the main aim of the bibliometric analysis?

To examine the evolution of prostate cancer research and assess how machine learning and AI have impacted PCa diagnosis, classification, and treatment.

Which database and tools were used for the study?

Articles from 1997–2025 were retrieved from Web of Science Core Collection, and bibliometric analysis and visualization were performed with VOSviewer and Bibliometrix.

What overall trend does the analysis report for ML in prostate cancer?

ML-related applications for PCa diagnosis, risk stratification, and treatment planning have increased markedly, especially over the past five years.

Aug 2, 2026

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