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Automated IMRT planning with regional optimization using planning scripts

Intensity‐modulated radiation therapy (IMRT) has become a standard technique in radiation therapy for treating different types of cancers. Various class solutions have been developed for simple cases (e.g., localized prostate, whole breast) to generate IMRT plans efficiently. However, for more compl...

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Autores principales: Xhaferllari, Ilma, Wong, Eugene, Bzdusek, Karl, Lock, Michael, Chen, Jeff Z.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714048/
https://www.ncbi.nlm.nih.gov/pubmed/23318393
http://dx.doi.org/10.1120/jacmp.v14i1.4052
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author Xhaferllari, Ilma
Wong, Eugene
Bzdusek, Karl
Lock, Michael
Chen, Jeff Z.
author_facet Xhaferllari, Ilma
Wong, Eugene
Bzdusek, Karl
Lock, Michael
Chen, Jeff Z.
author_sort Xhaferllari, Ilma
collection PubMed
description Intensity‐modulated radiation therapy (IMRT) has become a standard technique in radiation therapy for treating different types of cancers. Various class solutions have been developed for simple cases (e.g., localized prostate, whole breast) to generate IMRT plans efficiently. However, for more complex cases (e.g., head and neck, pelvic nodes), it can be time‐consuming for a planner to generate optimized IMRT plans. To generate optimal plans in these more complex cases which generally have multiple target volumes and organs at risk, it is often required to have additional IMRT optimization structures such as dose limiting ring structures, adjust beam geometry, select inverse planning objectives and associated weights, and additional IMRT objectives to reduce cold and hot spots in the dose distribution. These parameters are generally manually adjusted with a repeated trial and error approach during the optimization process. To improve IMRT planning efficiency in these more complex cases, an iterative method that incorporates some of these adjustment processes automatically in a planning script is designed, implemented, and validated. In particular, regional optimization has been implemented in an iterative way to reduce various hot or cold spots during the optimization process that begins with defining and automatic segmentation of hot and cold spots, introducing new objectives and their relative weights into inverse planning, and turn this into an iterative process with termination criteria. The method has been applied to three clinical sites: prostate with pelvic nodes, head and neck, and anal canal cancers, and has shown to reduce IMRT planning time significantly for clinical applications with improved plan quality. The IMRT planning scripts have been used for more than 500 clinical cases. PACS numbers: 87.55.D, 87.55.de
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spelling pubmed-57140482018-04-02 Automated IMRT planning with regional optimization using planning scripts Xhaferllari, Ilma Wong, Eugene Bzdusek, Karl Lock, Michael Chen, Jeff Z. J Appl Clin Med Phys Radiation Oncology Physics Intensity‐modulated radiation therapy (IMRT) has become a standard technique in radiation therapy for treating different types of cancers. Various class solutions have been developed for simple cases (e.g., localized prostate, whole breast) to generate IMRT plans efficiently. However, for more complex cases (e.g., head and neck, pelvic nodes), it can be time‐consuming for a planner to generate optimized IMRT plans. To generate optimal plans in these more complex cases which generally have multiple target volumes and organs at risk, it is often required to have additional IMRT optimization structures such as dose limiting ring structures, adjust beam geometry, select inverse planning objectives and associated weights, and additional IMRT objectives to reduce cold and hot spots in the dose distribution. These parameters are generally manually adjusted with a repeated trial and error approach during the optimization process. To improve IMRT planning efficiency in these more complex cases, an iterative method that incorporates some of these adjustment processes automatically in a planning script is designed, implemented, and validated. In particular, regional optimization has been implemented in an iterative way to reduce various hot or cold spots during the optimization process that begins with defining and automatic segmentation of hot and cold spots, introducing new objectives and their relative weights into inverse planning, and turn this into an iterative process with termination criteria. The method has been applied to three clinical sites: prostate with pelvic nodes, head and neck, and anal canal cancers, and has shown to reduce IMRT planning time significantly for clinical applications with improved plan quality. The IMRT planning scripts have been used for more than 500 clinical cases. PACS numbers: 87.55.D, 87.55.de John Wiley and Sons Inc. 2013-01-07 /pmc/articles/PMC5714048/ /pubmed/23318393 http://dx.doi.org/10.1120/jacmp.v14i1.4052 Text en © 2013 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
Xhaferllari, Ilma
Wong, Eugene
Bzdusek, Karl
Lock, Michael
Chen, Jeff Z.
Automated IMRT planning with regional optimization using planning scripts
title Automated IMRT planning with regional optimization using planning scripts
title_full Automated IMRT planning with regional optimization using planning scripts
title_fullStr Automated IMRT planning with regional optimization using planning scripts
title_full_unstemmed Automated IMRT planning with regional optimization using planning scripts
title_short Automated IMRT planning with regional optimization using planning scripts
title_sort automated imrt planning with regional optimization using planning scripts
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5714048/
https://www.ncbi.nlm.nih.gov/pubmed/23318393
http://dx.doi.org/10.1120/jacmp.v14i1.4052
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