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Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction
A recent publication indicated that the patient anatomical feature (PAF) model was capable of predicting optimal objectives based on past experience. In this study, the benefits of IMRT optimization using PAF‐predicted objectives as guidance for prostate were evaluated. Three different optimization...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2015
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5690098/ https://www.ncbi.nlm.nih.gov/pubmed/26103191 http://dx.doi.org/10.1120/jacmp.v16i2.5204 |
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author | Yang, Yun Li, Taoran Yuan, Lulin Ge, Yaorong Yin, Fang‐Fang Lee, W. Robert Wu, Q. Jackie |
author_facet | Yang, Yun Li, Taoran Yuan, Lulin Ge, Yaorong Yin, Fang‐Fang Lee, W. Robert Wu, Q. Jackie |
author_sort | Yang, Yun |
collection | PubMed |
description | A recent publication indicated that the patient anatomical feature (PAF) model was capable of predicting optimal objectives based on past experience. In this study, the benefits of IMRT optimization using PAF‐predicted objectives as guidance for prostate were evaluated. Three different optimization methods were compared. 1) Expert Plan: Ten prostate cases (16 plans) were planned by an expert planner using conventional trial‐and‐error approach started with institutional modified OAR and PTV constraints. Optimization was stopped at 150 iterations and that plan was saved as Expert Plan. 2) Clinical Plan: The planner would keep working on the Expert Plan till he was satisfied with the dosimetric quality and the final plan was referred to as Clinical Plan. 3) PAF Plan: A third sets of plans for the same ten patients were generated fully automatically using predicted DVHs as guidance. The optimization was based on PAF‐based predicted objectives, and was continued to 150 iterations without human interaction. [Formula: see text] and [Formula: see text] for PTV, [Formula: see text] for femoral heads, [Formula: see text] , D(10cc), [Formula: see text] , and [Formula: see text] for bladder/rectum were compared. Clinical Plans are further optimized with more iterations and adjustments, but in general provided limited dosimetric benefits over Expert Plans. PTV [Formula: see text] agreed within 2.31% among Expert, Clinical, and PAF plans. Between Clinical and PAF Plans, differences for [Formula: see text] of PTV, bladder, and rectum were within 2.65%, 2.46%, and 2.20%, respectively. Bladder D(10cc) was higher for PAF but [Formula: see text] in general. Bladder [Formula: see text] and [Formula: see text] were lower for PAF, by up to 7.71% and 6.81%, respectively. Rectum D(10cc), [Formula: see text] , and [Formula: see text] were 2.11%, 2.72%, and 0.27% lower for PAF, respectively. [Formula: see text] for femoral heads were comparable ([Formula: see text] on average). Compared to Clinical Plan ([Formula: see text]), the average optimization time for PAF plan was reduced by 5.2 min on average, with a maximum reduction of 7.1 min. Total numbers of MUs per plan for PAF Plans were lower than Clinical Plans, indicating better delivery efficiency. The PAF‐guided planning process is capable of generating clinical‐quality prostate IMRT plans with no human intervention. Compared to manual optimization, this automatic optimization increases planning and delivery efficiency, while maintaining plan quality. PACS numbers: 87.55.D‐, 87.55.de, 87.53.Jw |
format | Online Article Text |
id | pubmed-5690098 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2015 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-56900982018-04-02 Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction Yang, Yun Li, Taoran Yuan, Lulin Ge, Yaorong Yin, Fang‐Fang Lee, W. Robert Wu, Q. Jackie J Appl Clin Med Phys Radiation Oncology Physics A recent publication indicated that the patient anatomical feature (PAF) model was capable of predicting optimal objectives based on past experience. In this study, the benefits of IMRT optimization using PAF‐predicted objectives as guidance for prostate were evaluated. Three different optimization methods were compared. 1) Expert Plan: Ten prostate cases (16 plans) were planned by an expert planner using conventional trial‐and‐error approach started with institutional modified OAR and PTV constraints. Optimization was stopped at 150 iterations and that plan was saved as Expert Plan. 2) Clinical Plan: The planner would keep working on the Expert Plan till he was satisfied with the dosimetric quality and the final plan was referred to as Clinical Plan. 3) PAF Plan: A third sets of plans for the same ten patients were generated fully automatically using predicted DVHs as guidance. The optimization was based on PAF‐based predicted objectives, and was continued to 150 iterations without human interaction. [Formula: see text] and [Formula: see text] for PTV, [Formula: see text] for femoral heads, [Formula: see text] , D(10cc), [Formula: see text] , and [Formula: see text] for bladder/rectum were compared. Clinical Plans are further optimized with more iterations and adjustments, but in general provided limited dosimetric benefits over Expert Plans. PTV [Formula: see text] agreed within 2.31% among Expert, Clinical, and PAF plans. Between Clinical and PAF Plans, differences for [Formula: see text] of PTV, bladder, and rectum were within 2.65%, 2.46%, and 2.20%, respectively. Bladder D(10cc) was higher for PAF but [Formula: see text] in general. Bladder [Formula: see text] and [Formula: see text] were lower for PAF, by up to 7.71% and 6.81%, respectively. Rectum D(10cc), [Formula: see text] , and [Formula: see text] were 2.11%, 2.72%, and 0.27% lower for PAF, respectively. [Formula: see text] for femoral heads were comparable ([Formula: see text] on average). Compared to Clinical Plan ([Formula: see text]), the average optimization time for PAF plan was reduced by 5.2 min on average, with a maximum reduction of 7.1 min. Total numbers of MUs per plan for PAF Plans were lower than Clinical Plans, indicating better delivery efficiency. The PAF‐guided planning process is capable of generating clinical‐quality prostate IMRT plans with no human intervention. Compared to manual optimization, this automatic optimization increases planning and delivery efficiency, while maintaining plan quality. PACS numbers: 87.55.D‐, 87.55.de, 87.53.Jw John Wiley and Sons Inc. 2015-03-08 /pmc/articles/PMC5690098/ /pubmed/26103191 http://dx.doi.org/10.1120/jacmp.v16i2.5204 Text en © 2015 The Authors. This is an open access article under the terms of the Creative Commons Attribution (http://creativecommons.org/licenses/by/3.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Radiation Oncology Physics Yang, Yun Li, Taoran Yuan, Lulin Ge, Yaorong Yin, Fang‐Fang Lee, W. Robert Wu, Q. Jackie Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction |
title | Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction |
title_full | Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction |
title_fullStr | Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction |
title_full_unstemmed | Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction |
title_short | Quantitative comparison of automatic and manual IMRT optimization for prostate cancer: the benefits of DVH prediction |
title_sort | quantitative comparison of automatic and manual imrt optimization for prostate cancer: the benefits of dvh prediction |
topic | Radiation Oncology Physics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5690098/ https://www.ncbi.nlm.nih.gov/pubmed/26103191 http://dx.doi.org/10.1120/jacmp.v16i2.5204 |
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