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Development of Machine Learning Models for Predicting Prostate Cancer in Biopsy Candidates Using Prostate-Specific Antigen, Magnetic Resonance Imaging, and Hematologic Parameters.

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Bibliographic Details
Title: Development of Machine Learning Models for Predicting Prostate Cancer in Biopsy Candidates Using Prostate-Specific Antigen, Magnetic Resonance Imaging, and Hematologic Parameters.
Authors: Özlü DN; Department of Urology, Bakırköy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye., Arıkan Y; Department of Urology, Izmir Tepecik Training and Research Hospital, University of Health Sciences, Izmir, Türkiye., Emir B; Department of Biostatistics, Faculty of Medicine, İzmir Katip Celebi University, Izmir, Türkiye., Ayten A; Department of Urology, Gaziosmanpasa Training and Research Hospital, University of Health Sciences, İstanbul, Türkiye., Sungur U; Department of Urology, Bakırköy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye., Ekşi M; Department of Urology, Bakırköy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye., Polat H; Department of Urology, Bakırköy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye., Karadağ S; Department of Urology, Bakırköy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye., Bitkin A; Department of Urology, Bakırköy Dr. Sadi Konuk Training and Research Hospital, University of Health Sciences, Istanbul, Türkiye.
Source: The Prostate [Prostate] 2026 Jun; Vol. 86 (9), pp. 982-989. Date of Electronic Publication: 2026 Mar 30.
Publication Type: Journal Article
Journal Info: Publisher: Wiley-Liss Country of Publication: United States NLM ID: 8101368 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1097-0045 (Electronic) Linking ISSN: 02704137 NLM ISO Abbreviation: Prostate Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:1097-0045
DOI:10.1002/pros.70168