Development of Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer Patients Using Clinical Risk Factors, Patient-Reported Outcomes, and Serum Cytokine Biomarkers.

Journal: Clinical breast cancer
Published Date: Mar 5, 2025

Abstract

BACKGROUND: Radiation dermatitis (RD) is a significant side effect of radiotherapy experienced by breast cancer patients. Severe symptoms include desquamation or ulceration of irradiated skin, which impacts quality of life and increases healthcare costs. Early identification of patients at risk for severe RD can facilitate preventive management and reduce severe symptoms. This study evaluated the utility of subjective and objective factors, such as patient-reported outcomes (PROs) and serum cytokines, for predicting RD in breast cancer patients. The performance of machine learning (ML) and logistic regression-based models were compared.

Authors

  • Neil Lin
    Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada; Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
  • Farnoosh Abbas-Aghababazadeh
    Princess Margaret Cancer Centre, University Health Network, Toronto, Canada.
  • Jie Su
    School of Information Engineering, Suqian University, Suqian, Jiangsu, China.
  • Alison J Wu
    Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
  • Cherie Lin
    Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, Canada.
  • Wei Shi
    Department of Orthopedics, Shenzhen Pediatrics Institute of Shantou University Medical College, Shenzhen, China.
  • Wei Xu
    College of Food and Bioengineering, Henan University of Science and Technology, Luoyang, 471023 China.
  • Benjamin Haibe-Kains
    Princess Margaret Cancer Centre, University Health Network, Canada, Toronto, ON, Canada.
  • Fei-fei Liu
  • Jennifer Y Y Kwan
    Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada.

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