Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy

PurposeThis study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs).MethodsA convolu...

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Autores principales:	Yaoying Liu, Zhaocai Chen, Jinyuan Wang, Xiaoshen Wang, Baolin Qu, Lin Ma, Wei Zhao, Gaolong Zhang, Shouping Xu
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	dose prediction deep learning Tomotherapy nasopharyngeal carcinoma radiotherapy plan Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
Acceso en línea:	https://doaj.org/article/925c0598303342d7baadf347b825541e
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spelling	oai:doaj.org-article:925c0598303342d7baadf347b825541e2021-11-11T04:46:37ZDose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy2234-943X10.3389/fonc.2021.752007https://doaj.org/article/925c0598303342d7baadf347b825541e2021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fonc.2021.752007/fullhttps://doaj.org/toc/2234-943XPurposeThis study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs).MethodsA convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the “3D Dense-U-Net”, which combines the U-Net and the Dense-Net. To evaluate the model, we retrospectively used 124 NPC patients treated with Tomotherapy, in which 96 and 28 patients were randomly split and used for model training and test, respectively. We performed comparison studies using different training matrix shapes and dimensions for the CNN models, i.e., 128 ×128 ×48 (for Model I), 128 ×128 ×16 (for Model II), and 2D Dense U-Net (for Model III). The performance of these models was quantitatively evaluated using clinically relevant metrics and statistical analysis.ResultsWe found a more considerable height of the training patch size yields a better model outcome. The study calculated the corresponding errors by comparing the predicted dose with the ground truth. The mean deviations from the mean and maximum doses of PTVs and OARs were 2.42 and 2.93%. Error for the maximum dose of right optic nerves in Model I was 4.87 ± 6.88%, compared with 7.9 ± 6.8% in Model II (p=0.08) and 13.85 ± 10.97% in Model III (p<0.01); the Model I performed the best. The gamma passing rates of PTV60 for 3%/3 mm criteria was 83.6 ± 5.2% in Model I, compared with 75.9 ± 5.5% in Model II (p<0.001) and 77.2 ± 7.3% in Model III (p<0.01); the Model I also gave the best outcome. The prediction error of D95 for PTV60 was 0.64 ± 0.68% in Model I, compared with 2.04 ± 1.38% in Model II (p<0.01) and 1.05 ± 0.96% in Model III (p=0.01); the Model I was also the best one.ConclusionsIt is significant to train the dose prediction model by exploiting deep-learning techniques with various clinical logic concepts. Increasing the height (Y direction) of training patch size can improve the dose prediction accuracy of tiny OARs and the whole body. Our dose prediction network model provides a clinically acceptable result and a training strategy for a dose prediction model. It should be helpful to build automatic Tomotherapy planning.Yaoying LiuYaoying LiuZhaocai ChenJinyuan WangXiaoshen WangBaolin QuLin MaWei ZhaoGaolong ZhangShouping XuFrontiers Media S.A.articledose predictiondeep learningTomotherapynasopharyngeal carcinomaradiotherapy planNeoplasms. Tumors. Oncology. Including cancer and carcinogensRC254-282ENFrontiers in Oncology, Vol 11 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	dose prediction deep learning Tomotherapy nasopharyngeal carcinoma radiotherapy plan Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282
spellingShingle	dose prediction deep learning Tomotherapy nasopharyngeal carcinoma radiotherapy plan Neoplasms. Tumors. Oncology. Including cancer and carcinogens RC254-282 Yaoying Liu Yaoying Liu Zhaocai Chen Jinyuan Wang Xiaoshen Wang Baolin Qu Lin Ma Wei Zhao Gaolong Zhang Shouping Xu Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy
description	PurposeThis study focused on predicting 3D dose distribution at high precision and generated the prediction methods for nasopharyngeal carcinoma patients (NPC) treated with Tomotherapy based on the patient-specific gap between organs at risk (OARs) and planning target volumes (PTVs).MethodsA convolutional neural network (CNN) is trained using the CT and contour masks as the input and dose distributions as output. The CNN is based on the “3D Dense-U-Net”, which combines the U-Net and the Dense-Net. To evaluate the model, we retrospectively used 124 NPC patients treated with Tomotherapy, in which 96 and 28 patients were randomly split and used for model training and test, respectively. We performed comparison studies using different training matrix shapes and dimensions for the CNN models, i.e., 128 ×128 ×48 (for Model I), 128 ×128 ×16 (for Model II), and 2D Dense U-Net (for Model III). The performance of these models was quantitatively evaluated using clinically relevant metrics and statistical analysis.ResultsWe found a more considerable height of the training patch size yields a better model outcome. The study calculated the corresponding errors by comparing the predicted dose with the ground truth. The mean deviations from the mean and maximum doses of PTVs and OARs were 2.42 and 2.93%. Error for the maximum dose of right optic nerves in Model I was 4.87 ± 6.88%, compared with 7.9 ± 6.8% in Model II (p=0.08) and 13.85 ± 10.97% in Model III (p<0.01); the Model I performed the best. The gamma passing rates of PTV60 for 3%/3 mm criteria was 83.6 ± 5.2% in Model I, compared with 75.9 ± 5.5% in Model II (p<0.001) and 77.2 ± 7.3% in Model III (p<0.01); the Model I also gave the best outcome. The prediction error of D95 for PTV60 was 0.64 ± 0.68% in Model I, compared with 2.04 ± 1.38% in Model II (p<0.01) and 1.05 ± 0.96% in Model III (p=0.01); the Model I was also the best one.ConclusionsIt is significant to train the dose prediction model by exploiting deep-learning techniques with various clinical logic concepts. Increasing the height (Y direction) of training patch size can improve the dose prediction accuracy of tiny OARs and the whole body. Our dose prediction network model provides a clinically acceptable result and a training strategy for a dose prediction model. It should be helpful to build automatic Tomotherapy planning.
format	article
author	Yaoying Liu Yaoying Liu Zhaocai Chen Jinyuan Wang Xiaoshen Wang Baolin Qu Lin Ma Wei Zhao Gaolong Zhang Shouping Xu
author_facet	Yaoying Liu Yaoying Liu Zhaocai Chen Jinyuan Wang Xiaoshen Wang Baolin Qu Lin Ma Wei Zhao Gaolong Zhang Shouping Xu
author_sort	Yaoying Liu
title	Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy
title_short	Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy
title_full	Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy
title_fullStr	Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy
title_full_unstemmed	Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy
title_sort	dose prediction using a three-dimensional convolutional neural network for nasopharyngeal carcinoma with tomotherapy
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/925c0598303342d7baadf347b825541e
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Dose Prediction Using a Three-Dimensional Convolutional Neural Network for Nasopharyngeal Carcinoma With Tomotherapy

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