Prediction of toxicity outcomes following radiotherapy using deep learning-based models: A systematic review.

Journal: Cancer radiotherapie : journal de la Societe francaise de radiotherapie oncologique
Published Date: Jul 21, 2023

Abstract

PURPOSE: This study aims to perform a comprehensive systematic review of deep learning (DL) models in predicting RT-induced toxicity.

Authors

  • D Tan
    Department of General Surgery, Daping Hospital, Army Medical University, Chongqing, 400042, China.
  • N F Mohd Nasir
    Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia.
  • H Abdul Manan
    Functional Image Processing Laboratory, Department of Radiology, Universiti Kebangsaan Malaysia Medical Centre, Cheras, Kuala Lumpur 56000, Malaysia.
  • N Yahya
    Centre of Diagnostic, Therapeutic and Investigative Sciences (CODTIS). Faculty of Health Sciences, Universiti Kebangsaan Malaysia, Jalan Raja Muda Aziz, Kuala Lumpur 50300 Malaysia. Electronic address: azrulyahya@ukm.edu.my.

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