A Comparison of Systematic, Targeted, and Combined Biopsy Using Machine Learning for Prediction of Prostate Cancer Risk: A Multi-Center Study.

Journal: Medical principles and practice : international journal of the Kuwait University, Health Science Centre
PMID: 39047698

Abstract

OBJECTIVES: The aims of the study were to construct a new prognostic prediction model for detecting prostate cancer (PCa) patients using machine-learning (ML) techniques and to compare those models across systematic and target biopsy detection techniques.

Authors

  • Mostafa A Arafa
    The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
  • Islam Omar
    Klipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, New Mexico, USA.
  • Karim H Farhat
    The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
  • Mona Elshinawy
    Engineering Technology and Surveying Engineering Department, New Mexico State University, Las Cruces, New Mexico, USA.
  • Farrukh Khan
    Department of Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
  • Faisal A Alkhathami
    Department of Surgery, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
  • Alaa Mokhtar
    Department of Urology, King Faisal Specialist Hospital and Research Center, Riyadh, Saudi Arabia.
  • Abdulaziz Althunayan
    The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
  • Danny M Rabah
    The Cancer Research Chair, Surgery Department, College of Medicine, King Saud University, Riyadh, Saudi Arabia.
  • Abdel-Hameed A Badawy
    Klipsch School of Electrical and Computer Engineering, New Mexico State University, Las Cruces, New Mexico, USA.

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