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Automated field‐in‐field whole brain radiotherapy planning

PURPOSE: We developed and tested an automatic field‐in‐field (FIF) solution for whole‐brain radiotherapy (WBRT) planning that creates a homogeneous dose distribution by minimizing hotspots, resulting in clinically acceptable plans. METHODS: A configurable auto‐planning algorithm was developed to aut...

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Autores principales: Huang, Kai, Hernandez, Soleil, Wang, Chenyang, Nguyen, Callistus, Briere, Tina Marie, Cardenas, Carlos, Court, Laurence, Xiao, Yao
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924111/
https://www.ncbi.nlm.nih.gov/pubmed/36354957
http://dx.doi.org/10.1002/acm2.13819
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author Huang, Kai
Hernandez, Soleil
Wang, Chenyang
Nguyen, Callistus
Briere, Tina Marie
Cardenas, Carlos
Court, Laurence
Xiao, Yao
author_facet Huang, Kai
Hernandez, Soleil
Wang, Chenyang
Nguyen, Callistus
Briere, Tina Marie
Cardenas, Carlos
Court, Laurence
Xiao, Yao
author_sort Huang, Kai
collection PubMed
description PURPOSE: We developed and tested an automatic field‐in‐field (FIF) solution for whole‐brain radiotherapy (WBRT) planning that creates a homogeneous dose distribution by minimizing hotspots, resulting in clinically acceptable plans. METHODS: A configurable auto‐planning algorithm was developed to automatically generate FIF WBRT plans independent of the treatment planning system. Configurable parameters include the definition of hotspots, target volume, maximum number of subfields, and minimum number of monitor units per field. This algorithm iteratively identifies a hotspot, creates two opposing subfields, calculates the dose, and optimizes the beam weight based on user‐configured constraints of dose‐volume histogram coverage and least‐squared cost functions. The algorithm was retrospectively tested on 17 whole‐brain patients. First, an in‐house landmark‐based automated beam aperture technique was used to generate the treatment fields and initial plans. Second, the FIF algorithm was employed to optimize the plans using physician‐defined goals of 99.9% of the brain volume receiving 100% of the prescription dose (30 Gy in 10 fractions) and a target hotspot definition of 107% of the prescription dose. The final auto‐optimized plans were assessed for clinical acceptability by an experienced radiation oncologist using a five‐point scale. RESULTS: The FIF algorithm reduced the mean (± SD) plan hotspot percentage dose from 35.0 Gy (116.6%) ± 0.6 Gy (2.0%) to 32.6 Gy (108.8%) ± 0.4 Gy (1.2%). Also, it decreased the mean (± SD) hotspot V107% [cm(3)] from 959 ± 498 cm(3) to 145 ± 224 cm(3). On average, plans were produced in 16 min without any user intervention. Furthermore, 76.5% of the auto‐plans were clinically acceptable (needing no or minor stylistic edits), and all of them were clinically acceptable after minor clinically necessary edits. CONCLUSIONS: This algorithm successfully produced high‐quality WBRT plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow.
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spelling pubmed-99241112023-02-14 Automated field‐in‐field whole brain radiotherapy planning Huang, Kai Hernandez, Soleil Wang, Chenyang Nguyen, Callistus Briere, Tina Marie Cardenas, Carlos Court, Laurence Xiao, Yao J Appl Clin Med Phys Radiation Oncology Physics PURPOSE: We developed and tested an automatic field‐in‐field (FIF) solution for whole‐brain radiotherapy (WBRT) planning that creates a homogeneous dose distribution by minimizing hotspots, resulting in clinically acceptable plans. METHODS: A configurable auto‐planning algorithm was developed to automatically generate FIF WBRT plans independent of the treatment planning system. Configurable parameters include the definition of hotspots, target volume, maximum number of subfields, and minimum number of monitor units per field. This algorithm iteratively identifies a hotspot, creates two opposing subfields, calculates the dose, and optimizes the beam weight based on user‐configured constraints of dose‐volume histogram coverage and least‐squared cost functions. The algorithm was retrospectively tested on 17 whole‐brain patients. First, an in‐house landmark‐based automated beam aperture technique was used to generate the treatment fields and initial plans. Second, the FIF algorithm was employed to optimize the plans using physician‐defined goals of 99.9% of the brain volume receiving 100% of the prescription dose (30 Gy in 10 fractions) and a target hotspot definition of 107% of the prescription dose. The final auto‐optimized plans were assessed for clinical acceptability by an experienced radiation oncologist using a five‐point scale. RESULTS: The FIF algorithm reduced the mean (± SD) plan hotspot percentage dose from 35.0 Gy (116.6%) ± 0.6 Gy (2.0%) to 32.6 Gy (108.8%) ± 0.4 Gy (1.2%). Also, it decreased the mean (± SD) hotspot V107% [cm(3)] from 959 ± 498 cm(3) to 145 ± 224 cm(3). On average, plans were produced in 16 min without any user intervention. Furthermore, 76.5% of the auto‐plans were clinically acceptable (needing no or minor stylistic edits), and all of them were clinically acceptable after minor clinically necessary edits. CONCLUSIONS: This algorithm successfully produced high‐quality WBRT plans and can improve treatment planning efficiency when incorporated into an automatic planning workflow. John Wiley and Sons Inc. 2022-11-10 /pmc/articles/PMC9924111/ /pubmed/36354957 http://dx.doi.org/10.1002/acm2.13819 Text en © 2022 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals, LLC on behalf of The American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Radiation Oncology Physics
Huang, Kai
Hernandez, Soleil
Wang, Chenyang
Nguyen, Callistus
Briere, Tina Marie
Cardenas, Carlos
Court, Laurence
Xiao, Yao
Automated field‐in‐field whole brain radiotherapy planning
title Automated field‐in‐field whole brain radiotherapy planning
title_full Automated field‐in‐field whole brain radiotherapy planning
title_fullStr Automated field‐in‐field whole brain radiotherapy planning
title_full_unstemmed Automated field‐in‐field whole brain radiotherapy planning
title_short Automated field‐in‐field whole brain radiotherapy planning
title_sort automated field‐in‐field whole brain radiotherapy planning
topic Radiation Oncology Physics
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9924111/
https://www.ncbi.nlm.nih.gov/pubmed/36354957
http://dx.doi.org/10.1002/acm2.13819
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