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Fully automated treatment planning for MLC‐based robotic radiotherapy
PURPOSE: To propose and validate a fully automated multicriterial treatment planning solution for a CyberKnife® equipped with an InCise(TM) 2 multileaf collimator. METHODS: The AUTO BAO plans are generated using fully automated prioritized multicriterial optimization (AUTO MCO) of pencil‐beam fluenc...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457110/ https://www.ncbi.nlm.nih.gov/pubmed/34037258 http://dx.doi.org/10.1002/mp.14993 |
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author | Schipaanboord, Bastiaan W.K. Giżyńska, Marta K. Rossi, Linda de Vries, Kim C. Heijmen, Ben J.M. Breedveld, Sebastiaan |
author_facet | Schipaanboord, Bastiaan W.K. Giżyńska, Marta K. Rossi, Linda de Vries, Kim C. Heijmen, Ben J.M. Breedveld, Sebastiaan |
author_sort | Schipaanboord, Bastiaan W.K. |
collection | PubMed |
description | PURPOSE: To propose and validate a fully automated multicriterial treatment planning solution for a CyberKnife® equipped with an InCise(TM) 2 multileaf collimator. METHODS: The AUTO BAO plans are generated using fully automated prioritized multicriterial optimization (AUTO MCO) of pencil‐beam fluence maps with integrated noncoplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been developed in‐house. AUTO MCO generates for each patient a single, high‐quality Pareto‐optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil‐beams, segment dose depositions, and final dose calculations are performed with a stand‐alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry. RESULTS: AUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (−49%)), segments (59.5/98.9 (−40%)), and delivery times (17.1/22.3 min. (−23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (−21%)) and had on average shorter delivery times (18.6/22.3 min. (−17%)). Delivered Monitor Units (MU) were similar for all three planning approaches. CONCLUSIONS: A new, vendor‐independent optimization workflow for fully automated generation of deliverable high‐quality CyberKnife® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times were reduced by 5.3 min. (−23%) due to large reductions in beam and segment numbers. |
format | Online Article Text |
id | pubmed-8457110 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-84571102021-09-27 Fully automated treatment planning for MLC‐based robotic radiotherapy Schipaanboord, Bastiaan W.K. Giżyńska, Marta K. Rossi, Linda de Vries, Kim C. Heijmen, Ben J.M. Breedveld, Sebastiaan Med Phys THERAPEUTIC INTERVENTIONS PURPOSE: To propose and validate a fully automated multicriterial treatment planning solution for a CyberKnife® equipped with an InCise(TM) 2 multileaf collimator. METHODS: The AUTO BAO plans are generated using fully automated prioritized multicriterial optimization (AUTO MCO) of pencil‐beam fluence maps with integrated noncoplanar beam angle optimization (BAO), followed by MLC segment generation. Both the AUTO MCO and segmentation algorithms have been developed in‐house. AUTO MCO generates for each patient a single, high‐quality Pareto‐optimal IMRT plan. The segmentation algorithm then accurately mimics the AUTO MCO 3D dose distribution, while considering all candidate beams simultaneously, rather than replicating the fluence maps. Pencil‐beams, segment dose depositions, and final dose calculations are performed with a stand‐alone version of the clinical dose calculation engine. For validation, AUTO BAO plans were generated for 33 prostate SBRT patients and compared to reference plans (REF) that were manually generated with the commercial treatment planning system (TPS), in absence of time pressure. REF plans were also compared to AUTO RB plans, for which fluence map optimization was performed for the beam angle configuration used in the REF plan, and the segmentation could use all these beams or only a subset, depending on the dosimetry. RESULTS: AUTO BAO plans were clinically acceptable and dosimetrically similar to REF plans, but had on average reduced numbers of beams ((beams in AUTO BAO)/(beams in REF) (relative improvement): 24.7/48.3 (−49%)), segments (59.5/98.9 (−40%)), and delivery times (17.1/22.3 min. (−23%)). Dosimetry of AUTO RB and REF were also similar, but AUTO RB used on average fewer beams (38.0/48.3 (−21%)) and had on average shorter delivery times (18.6/22.3 min. (−17%)). Delivered Monitor Units (MU) were similar for all three planning approaches. CONCLUSIONS: A new, vendor‐independent optimization workflow for fully automated generation of deliverable high‐quality CyberKnife® plans was proposed, including BAO. Compared to manual planning with the commercial TPS, fraction delivery times were reduced by 5.3 min. (−23%) due to large reductions in beam and segment numbers. John Wiley and Sons Inc. 2021-06-28 2021-08 /pmc/articles/PMC8457110/ /pubmed/34037258 http://dx.doi.org/10.1002/mp.14993 Text en © 2021 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine. https://creativecommons.org/licenses/by-nc/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by-nc/4.0/ (https://creativecommons.org/licenses/by-nc/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. |
spellingShingle | THERAPEUTIC INTERVENTIONS Schipaanboord, Bastiaan W.K. Giżyńska, Marta K. Rossi, Linda de Vries, Kim C. Heijmen, Ben J.M. Breedveld, Sebastiaan Fully automated treatment planning for MLC‐based robotic radiotherapy |
title | Fully automated treatment planning for MLC‐based robotic radiotherapy |
title_full | Fully automated treatment planning for MLC‐based robotic radiotherapy |
title_fullStr | Fully automated treatment planning for MLC‐based robotic radiotherapy |
title_full_unstemmed | Fully automated treatment planning for MLC‐based robotic radiotherapy |
title_short | Fully automated treatment planning for MLC‐based robotic radiotherapy |
title_sort | fully automated treatment planning for mlc‐based robotic radiotherapy |
topic | THERAPEUTIC INTERVENTIONS |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8457110/ https://www.ncbi.nlm.nih.gov/pubmed/34037258 http://dx.doi.org/10.1002/mp.14993 |
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