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Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients

BACKGROUND: Lung dose-volume histogram (DVH) in radiotherapy could be calculated from multiple normal lung definitions. The lung dosimetric parameters generated from various approaches are significantly different. However, limited evidence shows which definition should be used to more accurately pre...

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Autores principales: Meng, Yinnan, Yang, Haihua, Wang, Wei, Tang, Xingni, Jiang, Caiping, Shen, Yichao, Luo, Wei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332547/
https://www.ncbi.nlm.nih.gov/pubmed/30642354
http://dx.doi.org/10.1186/s13014-018-1204-x
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author Meng, Yinnan
Yang, Haihua
Wang, Wei
Tang, Xingni
Jiang, Caiping
Shen, Yichao
Luo, Wei
author_facet Meng, Yinnan
Yang, Haihua
Wang, Wei
Tang, Xingni
Jiang, Caiping
Shen, Yichao
Luo, Wei
author_sort Meng, Yinnan
collection PubMed
description BACKGROUND: Lung dose-volume histogram (DVH) in radiotherapy could be calculated from multiple normal lung definitions. The lung dosimetric parameters generated from various approaches are significantly different. However, limited evidence shows which definition should be used to more accurately predict radiation pneumonitis (RP). We aimed to compare the RP prediction accuracy of dosimetric parameters from three lung volume methods in lung cancer patients treated with Intensity-Modulated Radiation Therapy (IMRT). METHODS: We retrospectively reviewed 183 consecutive lung cancer patients treated with IMRT from January 2014 to October 2017. The normal lungs were defined by total bilateral lung volume (Total Lung), excluding PTV (Lung-PTV) or PGTV (Lung-PGTV). V5, V20, and mean lung dose (MLD) have been extracted from three definitions. The primary endpoint was acute grade 2 or higher RP (RP2). Correlation between RP2 and dose parameters were analyzed by logistic regression. We evaluated prediction performance using area under the receiver operating characteristic curve (AUC) and normal tissue complication probability (NTCP) model. RESULTS: Twenty-six patients (14.2%) developed acute RP2 after IMRT treatment. Significant dosimetric differences were found between any 2-paired lung volumes (Ps < 0.001). To limit RP2 incidence less than 20%, the cutoff MLDs were 12.5 Gy, 14.2 Gy, and 15.0 Gy, respectively, for Lung-PTV, Lung-PGTV, and Total Lung methods. There were 54% (13% vs. 20%) and 45% (20% vs. 29%) RP2 probability variances detected at each MLD cutoff points from Lung-PTV and Lung-PGTV definitions. The best RP prediction performance was found in MLD from Lung-PTV method (AUC = 0.647), which is significantly better (P = 0.006) than the MLD from Lung-PGTV method (AUC = 0.609). CONCLUSION: There are significant differences in acute RP2 rate prediction using dosimetric parameters from various normal lung definitions. Excluding PTV from total lung volume may be more accurate and promising to predict acute symptomatic radiation pneumonitis in IMRT treated lung cancer patients.
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spelling pubmed-63325472019-01-16 Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients Meng, Yinnan Yang, Haihua Wang, Wei Tang, Xingni Jiang, Caiping Shen, Yichao Luo, Wei Radiat Oncol Research BACKGROUND: Lung dose-volume histogram (DVH) in radiotherapy could be calculated from multiple normal lung definitions. The lung dosimetric parameters generated from various approaches are significantly different. However, limited evidence shows which definition should be used to more accurately predict radiation pneumonitis (RP). We aimed to compare the RP prediction accuracy of dosimetric parameters from three lung volume methods in lung cancer patients treated with Intensity-Modulated Radiation Therapy (IMRT). METHODS: We retrospectively reviewed 183 consecutive lung cancer patients treated with IMRT from January 2014 to October 2017. The normal lungs were defined by total bilateral lung volume (Total Lung), excluding PTV (Lung-PTV) or PGTV (Lung-PGTV). V5, V20, and mean lung dose (MLD) have been extracted from three definitions. The primary endpoint was acute grade 2 or higher RP (RP2). Correlation between RP2 and dose parameters were analyzed by logistic regression. We evaluated prediction performance using area under the receiver operating characteristic curve (AUC) and normal tissue complication probability (NTCP) model. RESULTS: Twenty-six patients (14.2%) developed acute RP2 after IMRT treatment. Significant dosimetric differences were found between any 2-paired lung volumes (Ps < 0.001). To limit RP2 incidence less than 20%, the cutoff MLDs were 12.5 Gy, 14.2 Gy, and 15.0 Gy, respectively, for Lung-PTV, Lung-PGTV, and Total Lung methods. There were 54% (13% vs. 20%) and 45% (20% vs. 29%) RP2 probability variances detected at each MLD cutoff points from Lung-PTV and Lung-PGTV definitions. The best RP prediction performance was found in MLD from Lung-PTV method (AUC = 0.647), which is significantly better (P = 0.006) than the MLD from Lung-PGTV method (AUC = 0.609). CONCLUSION: There are significant differences in acute RP2 rate prediction using dosimetric parameters from various normal lung definitions. Excluding PTV from total lung volume may be more accurate and promising to predict acute symptomatic radiation pneumonitis in IMRT treated lung cancer patients. BioMed Central 2019-01-14 /pmc/articles/PMC6332547/ /pubmed/30642354 http://dx.doi.org/10.1186/s13014-018-1204-x Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Meng, Yinnan
Yang, Haihua
Wang, Wei
Tang, Xingni
Jiang, Caiping
Shen, Yichao
Luo, Wei
Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients
title Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients
title_full Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients
title_fullStr Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients
title_full_unstemmed Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients
title_short Excluding PTV from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients
title_sort excluding ptv from lung volume may better predict radiation pneumonitis for intensity modulated radiation therapy in lung cancer patients
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6332547/
https://www.ncbi.nlm.nih.gov/pubmed/30642354
http://dx.doi.org/10.1186/s13014-018-1204-x
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