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Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer
Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed....
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
SAGE Publications
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466025/ https://www.ncbi.nlm.nih.gov/pubmed/34519556 http://dx.doi.org/10.1177/00368504211038162 |
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author | Bai, Xue Liu, Ze Zhang, Jie Wang, Shengye Hou, Qing Shan, Guoping Chen, Ming Wang, Binbing |
author_facet | Bai, Xue Liu, Ze Zhang, Jie Wang, Shengye Hou, Qing Shan, Guoping Chen, Ming Wang, Binbing |
author_sort | Bai, Xue |
collection | PubMed |
description | Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk (p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model (p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs. |
format | Online Article Text |
id | pubmed-10466025 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | SAGE Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-104660252023-08-31 Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer Bai, Xue Liu, Ze Zhang, Jie Wang, Shengye Hou, Qing Shan, Guoping Chen, Ming Wang, Binbing Sci Prog Original Manuscript Fully convolutional networks were developed for predicting optimal dose distributions for patients with left-sided breast cancer and compared the prediction accuracy between two-dimensional and three-dimensional networks. Sixty cases treated with volumetric modulated arc radiotherapy were analyzed. Among them, 50 cases were randomly chosen to conform the training set, and the remaining 10 were to construct the test set. Two U-Net fully convolutional networks predicted the dose distributions, with two-dimensional and three-dimensional convolution kernels, respectively. Computed tomography images, delineated regions of interest, or their combination were considered as input data. The accuracy of predicted results was evaluated against the clinical dose. Most types of input data retrieved a similar dose to the ground truth for organs at risk (p > 0.05). Overall, the two-dimensional model had higher performance than the three-dimensional model (p < 0.05). Moreover, the two-dimensional region of interest input provided the best prediction results regarding the planning target volume mean percentage difference (2.40 ± 0.18%), heart mean percentage difference (4.28 ± 2.02%), and the gamma index at 80% of the prescription dose are with tolerances of 3 mm and 3% (0.85 ± 0.03), whereas the two-dimensional combined input provided the best prediction regarding ipsilateral lung mean percentage difference (4.16 ± 1.48%), lung mean percentage difference (2.41 ± 0.95%), spinal cord mean percentage difference (0.67 ± 0.40%), and 80% Dice similarity coefficient (0.94 ± 0.01). Statistically, the two-dimensional combined inputs achieved higher prediction accuracy regarding 80% Dice similarity coefficient than the two-dimensional region of interest input (0.94 ± 0.01 vs 0.92 ± 0.01, p < 0.05). The two-dimensional data model retrieves higher performance than its three-dimensional counterpart for dose prediction, especially when using region of interest and combined inputs. SAGE Publications 2021-09-14 /pmc/articles/PMC10466025/ /pubmed/34519556 http://dx.doi.org/10.1177/00368504211038162 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by-nc/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (https://creativecommons.org/licenses/by-nc/4.0/) which permits non-commercial use, reproduction and distribution of the work without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage). |
spellingShingle | Original Manuscript Bai, Xue Liu, Ze Zhang, Jie Wang, Shengye Hou, Qing Shan, Guoping Chen, Ming Wang, Binbing Comparing of two dimensional and three dimensional fully convolutional networks for radiotherapy dose prediction in left-sided breast cancer |
title | Comparing of two dimensional and three dimensional fully
convolutional networks for radiotherapy dose prediction in left-sided breast
cancer |
title_full | Comparing of two dimensional and three dimensional fully
convolutional networks for radiotherapy dose prediction in left-sided breast
cancer |
title_fullStr | Comparing of two dimensional and three dimensional fully
convolutional networks for radiotherapy dose prediction in left-sided breast
cancer |
title_full_unstemmed | Comparing of two dimensional and three dimensional fully
convolutional networks for radiotherapy dose prediction in left-sided breast
cancer |
title_short | Comparing of two dimensional and three dimensional fully
convolutional networks for radiotherapy dose prediction in left-sided breast
cancer |
title_sort | comparing of two dimensional and three dimensional fully
convolutional networks for radiotherapy dose prediction in left-sided breast
cancer |
topic | Original Manuscript |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10466025/ https://www.ncbi.nlm.nih.gov/pubmed/34519556 http://dx.doi.org/10.1177/00368504211038162 |
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