Machine learning-based radiomic models to predict intensity-modulated radiation therapy response, Gleason score and stage in prostate cancer.

Journal: La Radiologia medica
Published Date: Jan 3, 2019

Abstract

OBJECTIVE: To develop different radiomic models based on the magnetic resonance imaging (MRI) radiomic features and machine learning methods to predict early intensity-modulated radiation therapy (IMRT) response, Gleason scores (GS) and prostate cancer (Pca) stages.

Authors

  • Hamid Abdollahi
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Bahram Mofid
    Shohada-e-Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Isaac Shiri
    Biomedical and Health Informatics, Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran.
  • Abolfazl Razzaghdoust
    Urology and Nephrology Research Center, Student Research Committee, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Afshin Saadipoor
    Shohada-e-Tajrish Medical Center, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Arash Mahdavi
    Department of Radiology, Modarres Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
  • Hassan Maleki Galandooz
    Faculty of Computer Science and Engineering, Image Processing and Distributed System Lab, Shahid Beheshti University, Tehran, Iran.
  • Seied Rabi Mahdavi
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran. srmahdavi@hotmail.com.

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