Development and Optimization of a Machine-Learning Prediction Model for Acute Desquamation After Breast Radiation Therapy in the Multicenter REQUITE Cohort

PURPOSE: Some patients with breast cancer treated by surgery and radiation therapy experience clinically significant toxicity, which may adversely affect cosmesis and quality of life. There is a paucity of validated clinical prediction models for radiation toxicity. We used machine learning (ML) alg...

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Detalles Bibliográficos
Autores principales: Aldraimli, Mahmoud, Osman, Sarah, Grishchuck, Diana, Ingram, Samuel, Lyon, Robert, Mistry, Anil, Oliveira, Jorge, Samuel, Robert, Shelley, Leila E.A., Soria, Daniele, Dwek, Miriam V., Aguado-Barrera, Miguel E., Azria, David, Chang-Claude, Jenny, Dunning, Alison, Giraldo, Alexandra, Green, Sheryl, Gutiérrez-Enríquez, Sara, Herskind, Carsten, van Hulle, Hans, Lambrecht, Maarten, Lozza, Laura, Rancati, Tiziana, Reyes, Victoria, Rosenstein, Barry S., de Ruysscher, Dirk, de Santis, Maria C., Seibold, Petra, Sperk, Elena, Symonds, R. Paul, Stobart, Hilary, Taboada-Valadares, Begoña, Talbot, Christopher J., Vakaet, Vincent J.L., Vega, Ana, Veldeman, Liv, Veldwijk, Marlon R., Webb, Adam, Weltens, Caroline, West, Catharine M., Chaussalet, Thierry J., Rattay, Tim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Scientific Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133391/
https://www.ncbi.nlm.nih.gov/pubmed/35647396
http://dx.doi.org/10.1016/j.adro.2021.100890

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9133391/
https://www.ncbi.nlm.nih.gov/pubmed/35647396
http://dx.doi.org/10.1016/j.adro.2021.100890

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