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Paradigm Shift in Radiation Treatment Planning Over Multiple Treatment Modalities
The inverse planning simulated annealing optimization engine was used to develop a new method of incorporating biological parameters into radiation treatment planning. This method integrates optimization of a radiation schedule over multiple types of delivery methods into a single algorithm. We demo...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Medknow Publications & Media Pvt Ltd
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491309/ https://www.ncbi.nlm.nih.gov/pubmed/34703096 http://dx.doi.org/10.4103/jmp.JMP_66_20 |
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author | Insley, Benjamin Hsu, I-Chow Cunha, J. Adam Martin |
author_facet | Insley, Benjamin Hsu, I-Chow Cunha, J. Adam Martin |
author_sort | Insley, Benjamin |
collection | PubMed |
description | The inverse planning simulated annealing optimization engine was used to develop a new method of incorporating biological parameters into radiation treatment planning. This method integrates optimization of a radiation schedule over multiple types of delivery methods into a single algorithm. We demonstrate a general procedure of incorporating a functional biological dose model into the calculation of physical dose prescriptions. This paradigm differs from current practice in that it combines biology-informed dose constraints with a physical dose optimizer allowing for the comparison of treatment plans across multiple different radiation types and fractionation schemes. |
format | Online Article Text |
id | pubmed-8491309 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Medknow Publications & Media Pvt Ltd |
record_format | MEDLINE/PubMed |
spelling | pubmed-84913092021-10-25 Paradigm Shift in Radiation Treatment Planning Over Multiple Treatment Modalities Insley, Benjamin Hsu, I-Chow Cunha, J. Adam Martin J Med Phys Review Article The inverse planning simulated annealing optimization engine was used to develop a new method of incorporating biological parameters into radiation treatment planning. This method integrates optimization of a radiation schedule over multiple types of delivery methods into a single algorithm. We demonstrate a general procedure of incorporating a functional biological dose model into the calculation of physical dose prescriptions. This paradigm differs from current practice in that it combines biology-informed dose constraints with a physical dose optimizer allowing for the comparison of treatment plans across multiple different radiation types and fractionation schemes. Medknow Publications & Media Pvt Ltd 2021 2021-09-08 /pmc/articles/PMC8491309/ /pubmed/34703096 http://dx.doi.org/10.4103/jmp.JMP_66_20 Text en Copyright: © 2021 Journal of Medical Physics https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms. |
spellingShingle | Review Article Insley, Benjamin Hsu, I-Chow Cunha, J. Adam Martin Paradigm Shift in Radiation Treatment Planning Over Multiple Treatment Modalities |
title | Paradigm Shift in Radiation Treatment Planning Over Multiple Treatment Modalities |
title_full | Paradigm Shift in Radiation Treatment Planning Over Multiple Treatment Modalities |
title_fullStr | Paradigm Shift in Radiation Treatment Planning Over Multiple Treatment Modalities |
title_full_unstemmed | Paradigm Shift in Radiation Treatment Planning Over Multiple Treatment Modalities |
title_short | Paradigm Shift in Radiation Treatment Planning Over Multiple Treatment Modalities |
title_sort | paradigm shift in radiation treatment planning over multiple treatment modalities |
topic | Review Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8491309/ https://www.ncbi.nlm.nih.gov/pubmed/34703096 http://dx.doi.org/10.4103/jmp.JMP_66_20 |
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