TY - JOUR
T1 - Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer
T2 - A Scoping Review
AU - Meneses, Jayana Castelo Branco Cavalcante de
AU - Santos Neto, Abilio Torres Dos
AU - Domingos, Maria Aparecida Ferreira
AU - Barbosa, Pedro Luis Saraiva
AU - Castro, Régia Christina Moura Barbosa
AU - Cunha, Gilmara Holanda da
AU - Sixsmith, Judith
AU - Fernandes, Ana Fátima Carvalho
N1 - Copyright © 2026 Wolters Kluwer Health, Inc. All rights reserved.
PY - 2026/1/30
Y1 - 2026/1/30
N2 - 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.
AB - 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.
KW - artificial intelligence
KW - machine learning
KW - breast neoplasms
KW - radiotherapy
KW - radiation induced dermatitis
U2 - 10.1097/CIN.0000000000001484
DO - 10.1097/CIN.0000000000001484
M3 - Article
C2 - 41614671
SN - 1538-2931
SP - 1
EP - 9
JO - Computers, informatics, nursing : CIN
JF - Computers, informatics, nursing : CIN
ER -
