Machine learning models for discriminating clinically significant from clinically insignificant prostate cancer using bi-parametric magnetic resonance imaging.

Journal: Diagnostic and interventional radiology (Ankara, Turkey)
Published Date: Oct 1, 2024

Abstract

PURPOSE: This study aims to demonstrate the performance of machine learning algorithms to distinguish clinically significant prostate cancer (csPCa) from clinically insignificant prostate cancer (ciPCa) in prostate bi-parametric magnetic resonance imaging (MRI) using radiomics features.

Authors

  • Hakan Ayyıldız
    Kars Harakani State Hospital, Clinic of Radiology, Kars, Türkiye
  • Okan İnce
    Rush University Medical Center, Department of Radiology, Division of Vascular and Interventional Radiology, Chicago, Illinois
  • Esin Korkut
    University of Health Sciences Türkiye, Bağcılar Training and Research Hospital, Clinic of Radiology, İstanbul, Türkiye
  • Merve Gülbiz Dağoğlu Kartal
    İstanbul University, İstanbul Faculty of Medicine, Department of Radiology, İstanbul, Türkiye
  • Atadan Tunacı
    İstanbul University, İstanbul Faculty of Medicine, Department of Radiology, İstanbul, Türkiye
  • Sukru Mehmet Erturk
    Istanbul University, School of Medicine (Capa), Istanbul, Turkey.

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