Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

Abstract The purpose of this work is to evaluate the performance of applying patient dosimetric information induced by individual uniform-intensity radiation fields in organ-at risk (OAR) dose-volume histogram (DVH) prediction, and extend to DVH prediction of planning target volume (PTV). Ninety nas...

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Autores principales:	Sheng xiu Jiao, Ming li Wang, Li xin Chen, Xiao-wei Liu
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/92e4c017da9a40e6b823fcd287e42a91
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spelling	oai:doaj.org-article:92e4c017da9a40e6b823fcd287e42a912021-12-02T14:06:49ZEvaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning10.1038/s41598-021-82749-52045-2322https://doaj.org/article/92e4c017da9a40e6b823fcd287e42a912021-02-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82749-5https://doaj.org/toc/2045-2322Abstract The purpose of this work is to evaluate the performance of applying patient dosimetric information induced by individual uniform-intensity radiation fields in organ-at risk (OAR) dose-volume histogram (DVH) prediction, and extend to DVH prediction of planning target volume (PTV). Ninety nasopharyngeal cancer intensity-modulated radiation therapy (IMRT) plans and 60 rectal cancer volumetric modulated arc therapy (VMAT) plans were employed in this study. Of these, 20 nasopharyngeal cancer cases and 15 rectal cancer cases were randomly selected as the testing data. The DVH prediction was performed using two methods. One method applied the individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation and the other method applied the distance-to-target histogram and the conformal-plan-dose-volume histogram (DTH + CPDVH). The determination coefficient R2 and mean absolute error (MAE) were used to evaluate DVH prediction accuracy. The PTV DVH prediction was performed using the IDVHs. The PTV dose coverage was evaluated using D 98 , D 95 , D 1 and uniformity index (UI). The OAR dose was compared using the maximum dose, V 30 and V 40 . The significance of the results was examined with the Wilcoxon signed rank test. For PTV DVH prediction using IDVHs, the clinical plan and IDVHs prediction method achieved mean UI values of 1.07 and 1.06 for nasopharyngeal cancer, and 1.04 and 1.05 for rectal cancer, respectively. No significant difference was found between the clinical plan results and predicted results using the IDVHs method in achieving PTV dose coverage (D 98, D 95, D 1 and UI) for both nasopharyngeal cancer and rectal cancer (p-values ≥ 0.052). For OAR DVH prediction, no significant difference was found between the IDVHs and DTH + CPDVH methods for the R2, MAE, the maximum dose, V 30 and V 40 (p-values ≥ 0.087 for all OARs). This work evaluates the performance of dosimetric information of several individual fields with uniform-intensity radiation for DVH prediction, and extends its application to PTV DVH prediction. The results indicated that the IDVHs method is comparable to the DTH + CPDVH method in accurately predicting the OAR DVH. The IDVHs method quantified the input features of the PTV and showed reliable PTV DVH prediction, which is helpful for plan quality evaluation and plan generation.Sheng xiu JiaoMing li WangLi xin ChenXiao-wei LiuNature 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 Sheng xiu Jiao Ming li Wang Li xin Chen Xiao-wei Liu Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning
description	Abstract The purpose of this work is to evaluate the performance of applying patient dosimetric information induced by individual uniform-intensity radiation fields in organ-at risk (OAR) dose-volume histogram (DVH) prediction, and extend to DVH prediction of planning target volume (PTV). Ninety nasopharyngeal cancer intensity-modulated radiation therapy (IMRT) plans and 60 rectal cancer volumetric modulated arc therapy (VMAT) plans were employed in this study. Of these, 20 nasopharyngeal cancer cases and 15 rectal cancer cases were randomly selected as the testing data. The DVH prediction was performed using two methods. One method applied the individual dose-volume histograms (IDVHs) induced by a series of fields with uniform-intensity irradiation and the other method applied the distance-to-target histogram and the conformal-plan-dose-volume histogram (DTH + CPDVH). The determination coefficient R2 and mean absolute error (MAE) were used to evaluate DVH prediction accuracy. The PTV DVH prediction was performed using the IDVHs. The PTV dose coverage was evaluated using D 98 , D 95 , D 1 and uniformity index (UI). The OAR dose was compared using the maximum dose, V 30 and V 40 . The significance of the results was examined with the Wilcoxon signed rank test. For PTV DVH prediction using IDVHs, the clinical plan and IDVHs prediction method achieved mean UI values of 1.07 and 1.06 for nasopharyngeal cancer, and 1.04 and 1.05 for rectal cancer, respectively. No significant difference was found between the clinical plan results and predicted results using the IDVHs method in achieving PTV dose coverage (D 98, D 95, D 1 and UI) for both nasopharyngeal cancer and rectal cancer (p-values ≥ 0.052). For OAR DVH prediction, no significant difference was found between the IDVHs and DTH + CPDVH methods for the R2, MAE, the maximum dose, V 30 and V 40 (p-values ≥ 0.087 for all OARs). This work evaluates the performance of dosimetric information of several individual fields with uniform-intensity radiation for DVH prediction, and extends its application to PTV DVH prediction. The results indicated that the IDVHs method is comparable to the DTH + CPDVH method in accurately predicting the OAR DVH. The IDVHs method quantified the input features of the PTV and showed reliable PTV DVH prediction, which is helpful for plan quality evaluation and plan generation.
format	article
author	Sheng xiu Jiao Ming li Wang Li xin Chen Xiao-wei Liu
author_facet	Sheng xiu Jiao Ming li Wang Li xin Chen Xiao-wei Liu
author_sort	Sheng xiu Jiao
title	Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning
title_short	Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning
title_full	Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning
title_fullStr	Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning
title_full_unstemmed	Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning
title_sort	evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/92e4c017da9a40e6b823fcd287e42a91
work_keys_str_mv	AT shengxiujiao evaluationofdosevolumehistogrampredictionfororganatriskandplanningtargetvolumebasedonmachinelearning AT mingliwang evaluationofdosevolumehistogrampredictionfororganatriskandplanningtargetvolumebasedonmachinelearning AT lixinchen evaluationofdosevolumehistogrampredictionfororganatriskandplanningtargetvolumebasedonmachinelearning AT xiaoweiliu evaluationofdosevolumehistogrampredictionfororganatriskandplanningtargetvolumebasedonmachinelearning
_version_	1718391977561554944

Evaluation of dose-volume histogram prediction for organ-at risk and planning target volume based on machine learning

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