Volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer

Abstract This study aims to develop a volume-based algorithm (VBA) that can rapidly optimize rotating gantry arc angles and predict the lung V5 preceding the treatment planning. This phantom study was performed in the dynamic arc therapy planning systems for an esophageal cancer model. The angle of...

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Autores principales:	Kuan-Heng Lin, Chen-Xiong Hsu, Shan-Ying Wang, Greta S. P. Mok, Chiu-Han Chang, Hui-Ju Tien, Pei-Wei Shueng, Tung-Hsin Wu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/291e691fbc864f08abcefee5c5035f61
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spelling	oai:doaj.org-article:291e691fbc864f08abcefee5c5035f612021-12-02T11:02:18ZVolume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer10.1038/s41598-021-83682-32045-2322https://doaj.org/article/291e691fbc864f08abcefee5c5035f612021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-83682-3https://doaj.org/toc/2045-2322Abstract This study aims to develop a volume-based algorithm (VBA) that can rapidly optimize rotating gantry arc angles and predict the lung V5 preceding the treatment planning. This phantom study was performed in the dynamic arc therapy planning systems for an esophageal cancer model. The angle of rotation of the gantry around the isocenter as defined as arc angle (θA), ranging from 360° to 80° with an interval of 20°, resulting in 15 different θA of treatment plans. The corresponding predicted lung V5 was calculated by the VBA, the mean lung dose, lung V5, lung V20, mean heart dose, heart V30, the spinal cord maximum dose and conformity index were assessed from dose–volume histogram in the treatment plan. Correlations between the predicted lung V5 and the dosimetric indices were evaluated using Pearson’s correlation coefficient. The results showed that the predicted lung V5 and the lung V5 in the treatment plan were positively correlated (r = 0.996, p < 0.001). As the θA decreased, lung V5, lung V20, and the mean lung dose decreased while the mean heart dose, V30 and the spinal cord maximum dose increased. The V20 and the mean lung dose also showed high correlations with the predicted lung V5 (r = 0.974, 0.999, p < 0.001). This study successfully developed an efficient VBA to rapidly calculate the θA to predict the lung V5 and reduce the lung dose, with potentials to improve the current clinical practice of dynamic arc radiotherapy.Kuan-Heng LinChen-Xiong HsuShan-Ying WangGreta S. P. MokChiu-Han ChangHui-Ju TienPei-Wei ShuengTung-Hsin WuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-9 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Kuan-Heng Lin Chen-Xiong Hsu Shan-Ying Wang Greta S. P. Mok Chiu-Han Chang Hui-Ju Tien Pei-Wei Shueng Tung-Hsin Wu Volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer
description	Abstract This study aims to develop a volume-based algorithm (VBA) that can rapidly optimize rotating gantry arc angles and predict the lung V5 preceding the treatment planning. This phantom study was performed in the dynamic arc therapy planning systems for an esophageal cancer model. The angle of rotation of the gantry around the isocenter as defined as arc angle (θA), ranging from 360° to 80° with an interval of 20°, resulting in 15 different θA of treatment plans. The corresponding predicted lung V5 was calculated by the VBA, the mean lung dose, lung V5, lung V20, mean heart dose, heart V30, the spinal cord maximum dose and conformity index were assessed from dose–volume histogram in the treatment plan. Correlations between the predicted lung V5 and the dosimetric indices were evaluated using Pearson’s correlation coefficient. The results showed that the predicted lung V5 and the lung V5 in the treatment plan were positively correlated (r = 0.996, p < 0.001). As the θA decreased, lung V5, lung V20, and the mean lung dose decreased while the mean heart dose, V30 and the spinal cord maximum dose increased. The V20 and the mean lung dose also showed high correlations with the predicted lung V5 (r = 0.974, 0.999, p < 0.001). This study successfully developed an efficient VBA to rapidly calculate the θA to predict the lung V5 and reduce the lung dose, with potentials to improve the current clinical practice of dynamic arc radiotherapy.
format	article
author	Kuan-Heng Lin Chen-Xiong Hsu Shan-Ying Wang Greta S. P. Mok Chiu-Han Chang Hui-Ju Tien Pei-Wei Shueng Tung-Hsin Wu
author_facet	Kuan-Heng Lin Chen-Xiong Hsu Shan-Ying Wang Greta S. P. Mok Chiu-Han Chang Hui-Ju Tien Pei-Wei Shueng Tung-Hsin Wu
author_sort	Kuan-Heng Lin
title	Volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer
title_short	Volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer
title_full	Volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer
title_fullStr	Volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer
title_full_unstemmed	Volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer
title_sort	volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/291e691fbc864f08abcefee5c5035f61
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Volume-based algorithm of lung dose optimization in novel dynamic arc radiotherapy for esophageal cancer

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