Computers, informatics, nursing : CIN · 2026
Machine Learning Models for Predicting Radiation Dermatitis in Breast Cancer: A Scoping Review.
СтатьяScoping ReviewНазвание и аннотация: Перевод в очереди
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Meneses Jayana Castelo Branco Cavalcante de, Santos Neto Abilio Torres Dos, Domingos Maria Aparecida Ferreira, Barbosa Pedro Luis Saraiva, Castro Régia Christina Moura Barbosa, Cunha Gilmara Holanda da, Sixsmith Judith, Fernandes Ana Fátima Carvalho
Аннотация
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.
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.