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Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy

Purpose: To develop a multivariable normal tissue complication probability (NTCP) model to predict moderate to severe late rectal bleeding following intensity-modulated radiation therapy (IMRT). Methods and materials: Sixty-eight patients with localized prostate cancer treated by IMRT from 2008 to 2...

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Autores principales: Huang, Chun-Chieh, Chao, Pei-Ju, Guo, Shih-Sian, Wang, Chong-Jong, Luo, Hao-Lun, Su, Yu-Li, Lee, Tsair-Fwu, Fang, Fu-Min
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Ivyspring International Publisher 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584341/
https://www.ncbi.nlm.nih.gov/pubmed/31258765
http://dx.doi.org/10.7150/jca.29606
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author Huang, Chun-Chieh
Chao, Pei-Ju
Guo, Shih-Sian
Wang, Chong-Jong
Luo, Hao-Lun
Su, Yu-Li
Lee, Tsair-Fwu
Fang, Fu-Min
author_facet Huang, Chun-Chieh
Chao, Pei-Ju
Guo, Shih-Sian
Wang, Chong-Jong
Luo, Hao-Lun
Su, Yu-Li
Lee, Tsair-Fwu
Fang, Fu-Min
author_sort Huang, Chun-Chieh
collection PubMed
description Purpose: To develop a multivariable normal tissue complication probability (NTCP) model to predict moderate to severe late rectal bleeding following intensity-modulated radiation therapy (IMRT). Methods and materials: Sixty-eight patients with localized prostate cancer treated by IMRT from 2008 to 2011 were enrolled. The median follow-up time was 56 months. According to the criteria of D'Amico risk classifications, there were 9, 20 and 39 patients in low, intermediate and high-risk groups, respectively. Forty-two patients were combined with androgen deprivation therapy. Fifteen patients had suffered from grade 2 or more (grade 2+) late rectal bleeding. The numbers of predictors for a multivariable logistic regression NTCP model were determined by the least absolute shrinkage and selection operator (LASSO). Results: The most important predictors for late rectal bleeding ranked by LASSO were platelet count, risk group and the relative volume of rectum receiving at least 65 Gy (V(65)). The NTCP model of grade 2+ rectal bleeding was as follows: S = -17.49 + Platelets (1000/μL) * (-0.025) + Risk group * Corresponding coefficient (low-risk group = 0; intermediate-risk group = 19.07; high-risk group = 20.41) + V(65) * 0.045. Conclusions: A LASSO-based multivariable NTCP model comprising three important predictors (platelet count, risk group and V(65)) was established to predict the incidence of grade 2+ late rectal bleeding after IMRT.
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spelling pubmed-65843412019-06-28 Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy Huang, Chun-Chieh Chao, Pei-Ju Guo, Shih-Sian Wang, Chong-Jong Luo, Hao-Lun Su, Yu-Li Lee, Tsair-Fwu Fang, Fu-Min J Cancer Research Paper Purpose: To develop a multivariable normal tissue complication probability (NTCP) model to predict moderate to severe late rectal bleeding following intensity-modulated radiation therapy (IMRT). Methods and materials: Sixty-eight patients with localized prostate cancer treated by IMRT from 2008 to 2011 were enrolled. The median follow-up time was 56 months. According to the criteria of D'Amico risk classifications, there were 9, 20 and 39 patients in low, intermediate and high-risk groups, respectively. Forty-two patients were combined with androgen deprivation therapy. Fifteen patients had suffered from grade 2 or more (grade 2+) late rectal bleeding. The numbers of predictors for a multivariable logistic regression NTCP model were determined by the least absolute shrinkage and selection operator (LASSO). Results: The most important predictors for late rectal bleeding ranked by LASSO were platelet count, risk group and the relative volume of rectum receiving at least 65 Gy (V(65)). The NTCP model of grade 2+ rectal bleeding was as follows: S = -17.49 + Platelets (1000/μL) * (-0.025) + Risk group * Corresponding coefficient (low-risk group = 0; intermediate-risk group = 19.07; high-risk group = 20.41) + V(65) * 0.045. Conclusions: A LASSO-based multivariable NTCP model comprising three important predictors (platelet count, risk group and V(65)) was established to predict the incidence of grade 2+ late rectal bleeding after IMRT. Ivyspring International Publisher 2019-06-02 /pmc/articles/PMC6584341/ /pubmed/31258765 http://dx.doi.org/10.7150/jca.29606 Text en © Ivyspring International Publisher This is an open access article distributed under the terms of the Creative Commons Attribution (CC BY-NC) license (https://creativecommons.org/licenses/by-nc/4.0/). See http://ivyspring.com/terms for full terms and conditions.
spellingShingle Research Paper
Huang, Chun-Chieh
Chao, Pei-Ju
Guo, Shih-Sian
Wang, Chong-Jong
Luo, Hao-Lun
Su, Yu-Li
Lee, Tsair-Fwu
Fang, Fu-Min
Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy
title Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy
title_full Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy
title_fullStr Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy
title_full_unstemmed Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy
title_short Developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy
title_sort developing a multivariable normal tissue complication probability model to predict late rectal bleeding following intensity-modulated radiation therapy
topic Research Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6584341/
https://www.ncbi.nlm.nih.gov/pubmed/31258765
http://dx.doi.org/10.7150/jca.29606
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