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Clinical implementation of a knowledge based planning tool for prostate VMAT

BACKGROUND: A knowledge based planning tool has been developed and implemented for prostate VMAT radiotherapy plans providing a target average rectum dose value based on previously achievable values for similar rectum/PTV overlap. The purpose of this planning tool is to highlight sub-optimal clinica...

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Autores principales: Powis, Richard, Bird, Andrew, Brennan, Matthew, Hinks, Susan, Newman, Hannah, Reed, Katie, Sage, John, Webster, Gareth
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423022/
https://www.ncbi.nlm.nih.gov/pubmed/28482845
http://dx.doi.org/10.1186/s13014-017-0814-z
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author Powis, Richard
Bird, Andrew
Brennan, Matthew
Hinks, Susan
Newman, Hannah
Reed, Katie
Sage, John
Webster, Gareth
author_facet Powis, Richard
Bird, Andrew
Brennan, Matthew
Hinks, Susan
Newman, Hannah
Reed, Katie
Sage, John
Webster, Gareth
author_sort Powis, Richard
collection PubMed
description BACKGROUND: A knowledge based planning tool has been developed and implemented for prostate VMAT radiotherapy plans providing a target average rectum dose value based on previously achievable values for similar rectum/PTV overlap. The purpose of this planning tool is to highlight sub-optimal clinical plans and to improve plan quality and consistency. METHODS: A historical cohort of 97 VMAT prostate plans was interrogated using a RayStation script and used to develop a local model for predicting optimum average rectum dose based on individual anatomy. A preliminary validation study was performed whereby historical plans identified as “optimal” and “sub-optimal” by the local model were replanned in a blinded study by four experienced planners and compared to the original clinical plan to assess whether any improvement in rectum dose was observed. The predictive model was then incorporated into a RayStation script and used as part of the clinical planning process. Planners were asked to use the script during planning to provide a patient specific prediction for optimum average rectum dose and to optimise the plan accordingly. RESULTS: Plans identified as “sub-optimal” in the validation study observed a statistically significant improvement in average rectum dose compared to the clinical plan when replanned whereas plans that were identified as “optimal” observed no improvement when replanned. This provided confidence that the local model can identify plans that were suboptimal in terms of rectal sparing. Clinical implementation of the knowledge based planning tool reduced the population-averaged mean rectum dose by 5.6Gy. There was a small but statistically significant increase in total MU and femoral head dose and a reduction in conformity index. These did not affect the clinical acceptability of the plans and no significant changes to other plan quality metrics were observed. CONCLUSIONS: The knowledge-based planning tool has enabled substantial reductions in population-averaged mean rectum dose for prostate VMAT patients. This suggests plans are improved when planners receive quantitative feedback on plan quality against historical data.
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spelling pubmed-54230222017-05-10 Clinical implementation of a knowledge based planning tool for prostate VMAT Powis, Richard Bird, Andrew Brennan, Matthew Hinks, Susan Newman, Hannah Reed, Katie Sage, John Webster, Gareth Radiat Oncol Research BACKGROUND: A knowledge based planning tool has been developed and implemented for prostate VMAT radiotherapy plans providing a target average rectum dose value based on previously achievable values for similar rectum/PTV overlap. The purpose of this planning tool is to highlight sub-optimal clinical plans and to improve plan quality and consistency. METHODS: A historical cohort of 97 VMAT prostate plans was interrogated using a RayStation script and used to develop a local model for predicting optimum average rectum dose based on individual anatomy. A preliminary validation study was performed whereby historical plans identified as “optimal” and “sub-optimal” by the local model were replanned in a blinded study by four experienced planners and compared to the original clinical plan to assess whether any improvement in rectum dose was observed. The predictive model was then incorporated into a RayStation script and used as part of the clinical planning process. Planners were asked to use the script during planning to provide a patient specific prediction for optimum average rectum dose and to optimise the plan accordingly. RESULTS: Plans identified as “sub-optimal” in the validation study observed a statistically significant improvement in average rectum dose compared to the clinical plan when replanned whereas plans that were identified as “optimal” observed no improvement when replanned. This provided confidence that the local model can identify plans that were suboptimal in terms of rectal sparing. Clinical implementation of the knowledge based planning tool reduced the population-averaged mean rectum dose by 5.6Gy. There was a small but statistically significant increase in total MU and femoral head dose and a reduction in conformity index. These did not affect the clinical acceptability of the plans and no significant changes to other plan quality metrics were observed. CONCLUSIONS: The knowledge-based planning tool has enabled substantial reductions in population-averaged mean rectum dose for prostate VMAT patients. This suggests plans are improved when planners receive quantitative feedback on plan quality against historical data. BioMed Central 2017-05-08 /pmc/articles/PMC5423022/ /pubmed/28482845 http://dx.doi.org/10.1186/s13014-017-0814-z Text en © The Author(s). 2017 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. 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.
spellingShingle Research
Powis, Richard
Bird, Andrew
Brennan, Matthew
Hinks, Susan
Newman, Hannah
Reed, Katie
Sage, John
Webster, Gareth
Clinical implementation of a knowledge based planning tool for prostate VMAT
title Clinical implementation of a knowledge based planning tool for prostate VMAT
title_full Clinical implementation of a knowledge based planning tool for prostate VMAT
title_fullStr Clinical implementation of a knowledge based planning tool for prostate VMAT
title_full_unstemmed Clinical implementation of a knowledge based planning tool for prostate VMAT
title_short Clinical implementation of a knowledge based planning tool for prostate VMAT
title_sort clinical implementation of a knowledge based planning tool for prostate vmat
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5423022/
https://www.ncbi.nlm.nih.gov/pubmed/28482845
http://dx.doi.org/10.1186/s13014-017-0814-z
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