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.

<h4>Purpose</h4>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) patient...

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Autores principales:	Tsair-Fwu Lee, Pei-Ju Chao, Hui-Min Ting, Liyun Chang, Yu-Jie Huang, Jia-Ming Wu, Hung-Yu Wang, Mong-Fong Horng, Chun-Ming Chang, Jen-Hong Lan, Ya-Yu Huang, Fu-Min Fang, Stephen Wan Leung
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2014
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/7cc338c7e96c40adb68d93ff2f34ced3
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spelling	oai:doaj.org-article:7cc338c7e96c40adb68d93ff2f34ced32021-11-18T08:30:30ZUsing 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.1932-620310.1371/journal.pone.0089700https://doaj.org/article/7cc338c7e96c40adb68d93ff2f34ced32014-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/24586971/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203<h4>Purpose</h4>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.<h4>Methods and materials</h4>Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3(+) xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R(2), chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC.<h4>Results</h4>Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R(2) was satisfactory and corresponded well with the expected values.<h4>Conclusions</h4>Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.Tsair-Fwu LeePei-Ju ChaoHui-Min TingLiyun ChangYu-Jie HuangJia-Ming WuHung-Yu WangMong-Fong HorngChun-Ming ChangJen-Hong LanYa-Yu HuangFu-Min FangStephen Wan LeungPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 9, Iss 2, p e89700 (2014)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tsair-Fwu Lee Pei-Ju Chao Hui-Min Ting Liyun Chang Yu-Jie Huang Jia-Ming Wu Hung-Yu Wang Mong-Fong Horng Chun-Ming Chang Jen-Hong Lan Ya-Yu Huang Fu-Min Fang Stephen Wan Leung 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.
description	<h4>Purpose</h4>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.<h4>Methods and materials</h4>Quality of life questionnaire datasets from 206 patients with HNC were analyzed. The European Organization for Research and Treatment of Cancer QLQ-H&N35 and QLQ-C30 questionnaires were used as the endpoint evaluation. The primary endpoint (grade 3(+) xerostomia) was defined as moderate-to-severe xerostomia at 3 (XER3m) and 12 months (XER12m) after the completion of IMRT. Normal tissue complication probability (NTCP) models were developed. The optimal and suboptimal numbers of prognostic factors for a multivariate logistic regression model were determined using the LASSO with bootstrapping technique. Statistical analysis was performed using the scaled Brier score, Nagelkerke R(2), chi-squared test, Omnibus, Hosmer-Lemeshow test, and the AUC.<h4>Results</h4>Eight prognostic factors were selected by LASSO for the 3-month time point: Dmean-c, Dmean-i, age, financial status, T stage, AJCC stage, smoking, and education. Nine prognostic factors were selected for the 12-month time point: Dmean-i, education, Dmean-c, smoking, T stage, baseline xerostomia, alcohol abuse, family history, and node classification. In the selection of the suboptimal number of prognostic factors by LASSO, three suboptimal prognostic factors were fine-tuned by Hosmer-Lemeshow test and AUC, i.e., Dmean-c, Dmean-i, and age for the 3-month time point. Five suboptimal prognostic factors were also selected for the 12-month time point, i.e., Dmean-i, education, Dmean-c, smoking, and T stage. The overall performance for both time points of the NTCP model in terms of scaled Brier score, Omnibus, and Nagelkerke R(2) was satisfactory and corresponded well with the expected values.<h4>Conclusions</h4>Multivariate NTCP models with LASSO can be used to predict patient-rated xerostomia after IMRT.
format	article
author	Tsair-Fwu Lee Pei-Ju Chao Hui-Min Ting Liyun Chang Yu-Jie Huang Jia-Ming Wu Hung-Yu Wang Mong-Fong Horng Chun-Ming Chang Jen-Hong Lan Ya-Yu Huang Fu-Min Fang Stephen Wan Leung
author_facet	Tsair-Fwu Lee Pei-Ju Chao Hui-Min Ting Liyun Chang Yu-Jie Huang Jia-Ming Wu Hung-Yu Wang Mong-Fong Horng Chun-Ming Chang Jen-Hong Lan Ya-Yu Huang Fu-Min Fang Stephen Wan Leung
author_sort	Tsair-Fwu Lee
title	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.
title_short	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.
title_full	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.
title_fullStr	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.
title_full_unstemmed	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.
title_sort	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.
publisher	Public Library of Science (PLoS)
publishDate	2014
url	https://doaj.org/article/7cc338c7e96c40adb68d93ff2f34ced3
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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.

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