Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review.

[BACKGROUND] Artificial intelligence, particularly machine learning, has great potential to improve health outcomes, including predicting adverse conditions. In breast cancer, machine learning models can help personalize prevention strategies for radiation-induced cutaneous toxicity.

[METHODS] This scoping review aimed to explore machine learning models for predicting radiation dermatitis in women with breast cancer. Data collection was conducted in November 2023 from 7 electronic databases and gray literature, with no restrictions on publication year. Publication selection was supported by the RAYYAN reference manager, and ResearchRabbit software expanded the search.

[RESULTS] A total of 22 publications were included. The reviewed models primarily predicted acute radiation dermatitis using clinical predictors. Most studies used cross-validation, and class imbalance was observed. The predominant models were developed using the Random Forest algorithm, with the Bayesian Network emerging as the top-performing model, incorporating clinical, clinicopathological, demographic, radiomic, and dosimetric predictors.

[CONCLUSION] This review underscores the importance of further investigation into multiomic biomarkers and the establishment of minimum nursing databases to support predictive model development for radiation dermatitis in breast cancer patients.