Using Multivariate Regression Model with Least Absolute Shrinkage and Selection Operator (LASSO) to Predict the Incidence of Xerostomia after Intensity-Modulated Radiotherapy for Head and Neck Cancer

PURPOSE: The aim of this study was to develop a multivariate logistic regression model with least absolute shrinkage and selection operator (LASSO) to make valid predictions about the incidence of moderate-to-severe patient-rated xerostomia among head and neck cancer (HNC) patients treated with IMRT...

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Detalles Bibliográficos
Autores principales: Lee, Tsair-Fwu, Chao, Pei-Ju, Ting, Hui-Min, Chang, Liyun, Huang, Yu-Jie, Wu, Jia-Ming, Wang, Hung-Yu, Horng, Mong-Fong, Chang, Chun-Ming, Lan, Jen-Hong, Huang, Ya-Yu, Fang, Fu-Min, Leung, Stephen Wan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3938504/
https://www.ncbi.nlm.nih.gov/pubmed/24586971
http://dx.doi.org/10.1371/journal.pone.0089700

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3938504/
https://www.ncbi.nlm.nih.gov/pubmed/24586971
http://dx.doi.org/10.1371/journal.pone.0089700

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