The dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes

Abstract Multiple competing normal tissue complication probability (NTCP) models have been proposed for predicting symptomatic radiation-induced lung injury in human. In this paper we tested the efficacy of four common NTCP models applied quantitatively to sub-clinical X-ray computed tomography (CT)...

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Autores principales:	Dustin Begosh-Mayne, Shruti Siva Kumar, Steven Toffel, Paul Okunieff, Walter O’Dell
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/1426b03024384d6196d82df401bc6257
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spelling	oai:doaj.org-article:1426b03024384d6196d82df401bc62572021-12-02T16:31:46ZThe dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes10.1038/s41598-020-67499-02045-2322https://doaj.org/article/1426b03024384d6196d82df401bc62572020-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-67499-0https://doaj.org/toc/2045-2322Abstract Multiple competing normal tissue complication probability (NTCP) models have been proposed for predicting symptomatic radiation-induced lung injury in human. In this paper we tested the efficacy of four common NTCP models applied quantitatively to sub-clinical X-ray computed tomography (CT)-density changes in the lung following radiotherapy. Radiotherapy planning datasets and follow-up chest CTs were obtained in eight patients treated for targets within the lung or hilar region. Image pixel-wise radiation dose exposure versus change in observable CT Hounsfield units was recorded for early (2–5 months) and late (6–9 months) time-points. Four NTCP models, Lyman, Logistic, Weibull and Poisson, were fit to the population data. The quality of fits was assessed by five statistical criteria. All four models fit the data significantly (p < 0.05) well at early, late and cumulative time points. The Lyman model fitted best for early effects while the Weibull Model fitted best for late effects. No significant difference was found between the fits of the models and with respect to parameters D50 and γ50. The D50 estimates were more robust than γ50 to image registration error. For analyzing population-based sub-clinical CT pixel intensity-based dose response, all four models performed well.Dustin Begosh-MayneShruti Siva KumarSteven ToffelPaul OkunieffWalter O’DellNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 10, Iss 1, Pp 1-9 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Dustin Begosh-Mayne Shruti Siva Kumar Steven Toffel Paul Okunieff Walter O’Dell The dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes
description	Abstract Multiple competing normal tissue complication probability (NTCP) models have been proposed for predicting symptomatic radiation-induced lung injury in human. In this paper we tested the efficacy of four common NTCP models applied quantitatively to sub-clinical X-ray computed tomography (CT)-density changes in the lung following radiotherapy. Radiotherapy planning datasets and follow-up chest CTs were obtained in eight patients treated for targets within the lung or hilar region. Image pixel-wise radiation dose exposure versus change in observable CT Hounsfield units was recorded for early (2–5 months) and late (6–9 months) time-points. Four NTCP models, Lyman, Logistic, Weibull and Poisson, were fit to the population data. The quality of fits was assessed by five statistical criteria. All four models fit the data significantly (p < 0.05) well at early, late and cumulative time points. The Lyman model fitted best for early effects while the Weibull Model fitted best for late effects. No significant difference was found between the fits of the models and with respect to parameters D50 and γ50. The D50 estimates were more robust than γ50 to image registration error. For analyzing population-based sub-clinical CT pixel intensity-based dose response, all four models performed well.
format	article
author	Dustin Begosh-Mayne Shruti Siva Kumar Steven Toffel Paul Okunieff Walter O’Dell
author_facet	Dustin Begosh-Mayne Shruti Siva Kumar Steven Toffel Paul Okunieff Walter O’Dell
author_sort	Dustin Begosh-Mayne
title	The dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes
title_short	The dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes
title_full	The dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes
title_fullStr	The dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes
title_full_unstemmed	The dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes
title_sort	dose–response characteristics of four ntcp models: using a novel ct-based radiomic method to quantify radiation-induced lung density changes
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/1426b03024384d6196d82df401bc6257
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The dose–response characteristics of four NTCP models: using a novel CT-based radiomic method to quantify radiation-induced lung density changes

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