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Radiotherapy dose distribution prediction for breast cancer using deformable image registration
BACKGROUND: Radiotherapy treatment planning dose prediction can be used to ensure plan quality and guide automatic plan. One of the dose prediction methods is incorporating historical treatment planning data into algorithms to estimate the dose–volume histogram (DVH) of organ for new patients. Altho...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260772/ https://www.ncbi.nlm.nih.gov/pubmed/32471419 http://dx.doi.org/10.1186/s12938-020-00783-2 |
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author | Bai, Xue Wang, Binbing Wang, Shengye Wu, Zhangwen Gou, Chengjun Hou, Qing |
author_facet | Bai, Xue Wang, Binbing Wang, Shengye Wu, Zhangwen Gou, Chengjun Hou, Qing |
author_sort | Bai, Xue |
collection | PubMed |
description | BACKGROUND: Radiotherapy treatment planning dose prediction can be used to ensure plan quality and guide automatic plan. One of the dose prediction methods is incorporating historical treatment planning data into algorithms to estimate the dose–volume histogram (DVH) of organ for new patients. Although DVH is used extensively in treatment plan quality and radiotherapy prognosis evaluation, three-dimensional dose distribution can describe the radiation effects more explicitly. The purpose of this retrospective study was to predict the dose distribution of breast cancer radiotherapy by means of deformable registration into atlas images with historical treatment planning data that were considered to achieve expert level. The atlas cohort comprised 20 patients with left-sided breast cancer, previously treated by volumetric-modulated arc radiotherapy. The registration-based prediction technique was applied to 20 patients outside the atlas cohort. This study evaluated and compared three different approaches: registration to the most similar image from a dataset of individual atlas images (SIM), registration to all images from a database of individual atlas images with the average method (WEI_A), and the weighted method (WEI_F). The dose prediction performance of each strategy was quantified using nine metrics, including the region of interest dose error, 80% and 100% prescription area dice similarity coefficients (DSCs), and γ metrics. A Friedman test and a nonparametric exact Wilcoxon signed rank test were performed to compare the differences among groups. The clinical doses of all cases served as the gold standard. RESULTS: The WEI_F method could achieve superior dose prediction results to those of WEI_A. WEI_F outperformed SIM in the organ-at-risk mean absolute difference (MAD). When using the WEI_F method, the MAD values for the ipsilateral lung, heart, and whole lung were 197.9 ± 42.9, 166 ± 55.1, 122.3 ± 25.5, and 55.3 ± 42.2 cGy, respectively. Moreover, SIM exhibited superior prediction in the DSC and γ metrics. When using the SIM method, the means of the 80% and 100% prescription area DSCs, 33γ metric, and 55γ metric were 0.85 ± 0.05, 0.84 ± 0.05, 0.64 ± 0.13, and 0.84 ± 0.10, respectively. The plan target volume and spinal cord MAD when using SIM and WEI were 235.6 ± 158.4 cGy versus 227.4 ± 144.0 cGy ([Formula: see text] ) and 61.4 ± 44.9 cGy versus 55.3 ± 42.2 cGy ([Formula: see text] ), respectively. CONCLUSIONS: A predicted dose distribution that approximated the clinical plan could be generated using the methods presented in this study. |
format | Online Article Text |
id | pubmed-7260772 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72607722020-06-07 Radiotherapy dose distribution prediction for breast cancer using deformable image registration Bai, Xue Wang, Binbing Wang, Shengye Wu, Zhangwen Gou, Chengjun Hou, Qing Biomed Eng Online Research BACKGROUND: Radiotherapy treatment planning dose prediction can be used to ensure plan quality and guide automatic plan. One of the dose prediction methods is incorporating historical treatment planning data into algorithms to estimate the dose–volume histogram (DVH) of organ for new patients. Although DVH is used extensively in treatment plan quality and radiotherapy prognosis evaluation, three-dimensional dose distribution can describe the radiation effects more explicitly. The purpose of this retrospective study was to predict the dose distribution of breast cancer radiotherapy by means of deformable registration into atlas images with historical treatment planning data that were considered to achieve expert level. The atlas cohort comprised 20 patients with left-sided breast cancer, previously treated by volumetric-modulated arc radiotherapy. The registration-based prediction technique was applied to 20 patients outside the atlas cohort. This study evaluated and compared three different approaches: registration to the most similar image from a dataset of individual atlas images (SIM), registration to all images from a database of individual atlas images with the average method (WEI_A), and the weighted method (WEI_F). The dose prediction performance of each strategy was quantified using nine metrics, including the region of interest dose error, 80% and 100% prescription area dice similarity coefficients (DSCs), and γ metrics. A Friedman test and a nonparametric exact Wilcoxon signed rank test were performed to compare the differences among groups. The clinical doses of all cases served as the gold standard. RESULTS: The WEI_F method could achieve superior dose prediction results to those of WEI_A. WEI_F outperformed SIM in the organ-at-risk mean absolute difference (MAD). When using the WEI_F method, the MAD values for the ipsilateral lung, heart, and whole lung were 197.9 ± 42.9, 166 ± 55.1, 122.3 ± 25.5, and 55.3 ± 42.2 cGy, respectively. Moreover, SIM exhibited superior prediction in the DSC and γ metrics. When using the SIM method, the means of the 80% and 100% prescription area DSCs, 33γ metric, and 55γ metric were 0.85 ± 0.05, 0.84 ± 0.05, 0.64 ± 0.13, and 0.84 ± 0.10, respectively. The plan target volume and spinal cord MAD when using SIM and WEI were 235.6 ± 158.4 cGy versus 227.4 ± 144.0 cGy ([Formula: see text] ) and 61.4 ± 44.9 cGy versus 55.3 ± 42.2 cGy ([Formula: see text] ), respectively. CONCLUSIONS: A predicted dose distribution that approximated the clinical plan could be generated using the methods presented in this study. BioMed Central 2020-05-29 /pmc/articles/PMC7260772/ /pubmed/32471419 http://dx.doi.org/10.1186/s12938-020-00783-2 Text en © The Author(s) 2020 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. 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 in a credit line to the data. |
spellingShingle | Research Bai, Xue Wang, Binbing Wang, Shengye Wu, Zhangwen Gou, Chengjun Hou, Qing Radiotherapy dose distribution prediction for breast cancer using deformable image registration |
title | Radiotherapy dose distribution prediction for breast cancer using deformable image registration |
title_full | Radiotherapy dose distribution prediction for breast cancer using deformable image registration |
title_fullStr | Radiotherapy dose distribution prediction for breast cancer using deformable image registration |
title_full_unstemmed | Radiotherapy dose distribution prediction for breast cancer using deformable image registration |
title_short | Radiotherapy dose distribution prediction for breast cancer using deformable image registration |
title_sort | radiotherapy dose distribution prediction for breast cancer using deformable image registration |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7260772/ https://www.ncbi.nlm.nih.gov/pubmed/32471419 http://dx.doi.org/10.1186/s12938-020-00783-2 |
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