Machine-learning prediction model for acute skin toxicity after breast radiation therapy using spectrophotometry

PURPOSE: Radiation-induced skin toxicity is a common and distressing side effect of breast radiation therapy (RT). We investigated the use of quantitative spectrophotometric markers as input parameters in supervised machine learning models to develop a predictive model for acute radiation toxicity....

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Detalles Bibliográficos
Autores principales: Cilla, Savino, Romano, Carmela, Macchia, Gabriella, Boccardi, Mariangela, Pezzulla, Donato, Buwenge, Milly, Castelnuovo, Augusto Di, Bracone, Francesca, Curtis, Amalia De, Cerletti, Chiara, Iacoviello, Licia, Donati, Maria Benedetta, Deodato, Francesco, Morganti, Alessio Giuseppe
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Oncology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853396/
https://www.ncbi.nlm.nih.gov/pubmed/36686808
http://dx.doi.org/10.3389/fonc.2022.1044358

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9853396/
https://www.ncbi.nlm.nih.gov/pubmed/36686808
http://dx.doi.org/10.3389/fonc.2022.1044358

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