Noninvasive Fuhrman grading of clear cell renal cell carcinoma using computed tomography radiomic features and machine learning.

Journal: La Radiologia medica
Published Date: Mar 19, 2020

Abstract

PURPOSE: To identify optimal classification methods for computed tomography (CT) radiomics-based preoperative prediction of clear cell renal cell carcinoma (ccRCC) grade.

Authors

  • Mostafa Nazari
    Department of Biomedical Engineering and Medical Physics, School of Medicine, 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.
  • Ghasem Hajianfar
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran.
  • Niki Oveisi
    School of Population and Public Health, The University of British Columbia, BC V6T 1Z4, Canada.
  • Hamid Abdollahi
    Department of Medical Physics, School of Medicine, Iran University of Medical Sciences, Tehran, Iran.
  • Mohammad Reza Deevband
    Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran. mdeevband@sbmu.ac.ir.
  • Mehrdad Oveisi
    Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Science, Tehran, Iran; Department of Computer Science, University of British ColumbiaVancouver, BC V6T 1Z4, Canada.
  • Habib Zaidi
    Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva 4, Switzerland. habib.zaidi@hcuge.ch.

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