Radiomics analysis on CT images for prediction of radiation-induced kidney damage by machine learning models.

Journal: Computers in biology and medicine
Published Date: Apr 19, 2021

Abstract

INTRODUCTION: We aimed to assess the power of radiomic features based on computed tomography to predict risk of chronic kidney disease in patients undergoing radiation therapy of abdominal cancers.

Authors

  • Sepideh Amiri
    Department of Information Technology, Faculty of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. Electronic address: sepideh.amiri@ut.ac.ir.
  • Mina Akbarabadi
    Department of Information Technology, Faculty of Industrial Engineering, K. N. Toosi University of Technology, Tehran, Iran. Electronic address: m.akbarabadi@kntu.ac.ir.
  • Fatemeh Abdolali
    Department of Radiology and Diagnostic Imaging, Faculty of Medicine and Dentistry, Alberta University, Edmonton, AB, Canada. Electronic address: abdolali@ualberta.ca.
  • Alireza Nikoofar
    Department of Radiation Oncology, Faculty of Medicine, Iran University of Medical Sciences, Tehran, Iran. Electronic address: arnikoofar@gmail.com.
  • Azam Janati Esfahani
    Department of Medical Biotechnology, School of Paramedical Sciences and Cellular and Molecular Research Center, Research Institute for Prevention of Non-Communicable Diseases, Qazvin University of Medical Sciences, Qazvin, Iran. Electronic address: janaty.azam@gmail.com.
  • Susan Cheraghi
    Radiation Biology Research Center, Iran University of Medical Sciences, Tehran, Iran; Department of Radiation Sciences, Faculty of Allied Medicine, Iran University of Medical Sciences, Tehran, Iran. Electronic address: cheraghi.s@iums.ac.ir.

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