Incorporating spatial dose metrics in machine learning-based normal tissue complication probability (NTCP) models of severe acute dysphagia resulting from head and neck radiotherapy

Severe acute dysphagia commonly results from head and neck radiotherapy (RT). A model enabling prediction of severity of acute dysphagia for individual patients could guide clinical decision-making. Statistical associations between RT dose distributions and dysphagia could inform RT planning protoco...

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Detalles Bibliográficos
Autores principales: Dean, Jamie, Wong, Kee, Gay, Hiram, Welsh, Liam, Jones, Ann-Britt, Schick, Ulricke, Oh, Jung Hun, Apte, Aditya, Newbold, Kate, Bhide, Shreerang, Harrington, Kevin, Deasy, Joseph, Nutting, Christopher, Gulliford, Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2017
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796681/
https://www.ncbi.nlm.nih.gov/pubmed/29399642
http://dx.doi.org/10.1016/j.ctro.2017.11.009

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5796681/
https://www.ncbi.nlm.nih.gov/pubmed/29399642
http://dx.doi.org/10.1016/j.ctro.2017.11.009

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