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Area of Science:

  • Oncology
  • Radiotherapy
  • Artificial Intelligence

Background:

  • Machine learning (ML) offers potential for improving health outcomes by predicting adverse conditions.
  • ML models can personalize prevention strategies for radiation-induced cutaneous toxicity in breast cancer patients.

Purpose of the Study:

  • To conduct a scoping review of machine learning models for predicting radiation dermatitis in women undergoing breast cancer treatment.
  • To explore the current landscape of ML applications in predicting this common side effect.

Main Methods:

  • A comprehensive search of 7 electronic databases and gray literature was performed in November 2023.
  • RAYYAN reference manager and ResearchRabbit software were utilized for publication selection and search expansion.
  • No restrictions were placed on publication year.

Main Results:

  • Twenty-two publications were included in the review.
  • Most models predicted acute radiation dermatitis using clinical predictors and cross-validation.
  • Random Forest was a predominant algorithm, while Bayesian Network models incorporating diverse predictors showed superior performance.

Conclusions:

  • Further investigation into multiomic biomarkers is essential for enhancing predictive accuracy.
  • Establishing minimum nursing databases is recommended to support robust predictive model development.
  • Continued research is vital for advancing personalized care in radiation dermatitis management.