Navigating the gray zone: Machine learning can differentiate malignancy in PI-RADS 3 lesions.

Journal: Urologic oncology
Published Date: Sep 28, 2024

Abstract

INTRODUCTION: The objective of this study is to predict the probability of prostate cancer in PI-RADS 3 lesions using machine learning methods that incorporate clinical and mpMRI parameters.

Authors

  • Emre Altıntaş
    Faculty of Medicine, Department of Urology, Selcuk University, Tıp Fakültesi Alaeddin Keykubat Yerleşkesi Selçuklu, Konya, 42131, Turkey. dr.e.altintas@gmail.com.
  • Ali Şahin
    Faculty of Medicine, Selcuk University, Konya, Turkey.
  • Seyit Erol
    Department of Radiology, Selcuk University School of Medicine, Konya, Turkey.
  • Halil Özer
    Department of Radiology, Selcuk University School of Medicine, Konya, Turkey.
  • Murat Gül
    Faculty of Medicine, Department of Urology, Selcuk University, Tıp Fakültesi Alaeddin Keykubat Yerleşkesi Selçuklu, Konya, 42131, Turkey.
  • Ali Furkan Batur
    Faculty of Medicine, Department of Urology, Selcuk University, Tıp Fakültesi Alaeddin Keykubat Yerleşkesi Selçuklu, Konya, 42131, Turkey.
  • Mehmet Kaynar
    Faculty of Medicine, Department of Urology, Selcuk University, Tıp Fakültesi Alaeddin Keykubat Yerleşkesi Selçuklu, Konya, 42131, Turkey.
  • Özcan Kılıç
    Faculty of Medicine, Department of Urology, Selcuk University, Tıp Fakültesi Alaeddin Keykubat Yerleşkesi Selçuklu, Konya, 42131, Turkey.
  • Serdar Göktaş
    Faculty of Medicine, Department of Urology, Selcuk University, Tıp Fakültesi Alaeddin Keykubat Yerleşkesi Selçuklu, Konya, 42131, Turkey.

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