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Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors

BACKGROUND: To analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS). METHODS: CT datasets of twenty left breas...

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Autores principales: Fogliata, Antonella, Parabicoli, Sara, Paganini, Lucia, Reggiori, Giacomo, Lobefalo, Francesca, Cozzi, Luca, Franzese, Ciro, Franceschini, Davide, Spoto, Ruggero, Scorsetti, Marta
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
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Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724419/
https://www.ncbi.nlm.nih.gov/pubmed/36474297
http://dx.doi.org/10.1186/s13014-022-02172-6
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author Fogliata, Antonella
Parabicoli, Sara
Paganini, Lucia
Reggiori, Giacomo
Lobefalo, Francesca
Cozzi, Luca
Franzese, Ciro
Franceschini, Davide
Spoto, Ruggero
Scorsetti, Marta
author_facet Fogliata, Antonella
Parabicoli, Sara
Paganini, Lucia
Reggiori, Giacomo
Lobefalo, Francesca
Cozzi, Luca
Franzese, Ciro
Franceschini, Davide
Spoto, Ruggero
Scorsetti, Marta
author_sort Fogliata, Antonella
collection PubMed
description BACKGROUND: To analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS). METHODS: CT datasets of twenty left breast patients in deep-inspiration breath-hold were selected. Two VMAT plans, PartArc and AvoidArc, were manually generated with double arcs from ~ 300 to ~ 160°, with the second having an AS en-face to the breast to avoid contralateral breast and lung direct irradiation. Two RapidPlan models were generated from the two plan sets. The two models were evaluated in a closed loop to assess the model performance on plans where the AS were selected or not in the optimization. RESULTS: The PartArc plans model estimated DVHs comparable with the original plans. The AvoidArc plans model estimated a DVH pattern with two steps for the contralateral structures when the plan does not contain the AS selected in the optimization phase. This feature produced mean doses of the contralateral breast, averaged over all patients, of 0.4 ± 0.1 Gy, 0.6 ± 0.2 Gy, and 1.1 ± 0.2 Gy for the AvoidArc plan, AvoidArc model estimation, RapidPlan generated plan, respectively. The same figures for the contralateral lung were 0.3 ± 0.1 Gy, 1.6 ± 0.6 Gy, and 1.2 ± 0.5 Gy. The reason was found in the possible incorrect information extracted from the model training plans due to the lack of knowledge about the AS. Conversely, in the case of plans with AS set in the optimization generated with the same AvoidArc model, the estimated and resulting DVHs were comparable. Whenever the AvoidArc model was used to generate DVH estimation for a plan with AS, while the optimization was made on the plan without the AS, the optimizer evidentiated the limitation of a minimum dose rate of 0.2 MU/°, resulting in an increased dose to the contralateral structures respect to the estimation. CONCLUSIONS: The RapidPlan models for breast planning with VMAT can properly estimate organ at risk DVH. Attention has to be paid to the plan selection and usage for model training in the presence of avoidance sectors.
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spelling pubmed-97244192022-12-07 Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors Fogliata, Antonella Parabicoli, Sara Paganini, Lucia Reggiori, Giacomo Lobefalo, Francesca Cozzi, Luca Franzese, Ciro Franceschini, Davide Spoto, Ruggero Scorsetti, Marta Radiat Oncol Research BACKGROUND: To analyze RapidPlan knowledge-based models for DVH estimation of organs at risk from breast cancer VMAT plans presenting arc sectors en-face to the breast with zero dose rate, feature imposed during the optimization phase (avoidance sectors AS). METHODS: CT datasets of twenty left breast patients in deep-inspiration breath-hold were selected. Two VMAT plans, PartArc and AvoidArc, were manually generated with double arcs from ~ 300 to ~ 160°, with the second having an AS en-face to the breast to avoid contralateral breast and lung direct irradiation. Two RapidPlan models were generated from the two plan sets. The two models were evaluated in a closed loop to assess the model performance on plans where the AS were selected or not in the optimization. RESULTS: The PartArc plans model estimated DVHs comparable with the original plans. The AvoidArc plans model estimated a DVH pattern with two steps for the contralateral structures when the plan does not contain the AS selected in the optimization phase. This feature produced mean doses of the contralateral breast, averaged over all patients, of 0.4 ± 0.1 Gy, 0.6 ± 0.2 Gy, and 1.1 ± 0.2 Gy for the AvoidArc plan, AvoidArc model estimation, RapidPlan generated plan, respectively. The same figures for the contralateral lung were 0.3 ± 0.1 Gy, 1.6 ± 0.6 Gy, and 1.2 ± 0.5 Gy. The reason was found in the possible incorrect information extracted from the model training plans due to the lack of knowledge about the AS. Conversely, in the case of plans with AS set in the optimization generated with the same AvoidArc model, the estimated and resulting DVHs were comparable. Whenever the AvoidArc model was used to generate DVH estimation for a plan with AS, while the optimization was made on the plan without the AS, the optimizer evidentiated the limitation of a minimum dose rate of 0.2 MU/°, resulting in an increased dose to the contralateral structures respect to the estimation. CONCLUSIONS: The RapidPlan models for breast planning with VMAT can properly estimate organ at risk DVH. Attention has to be paid to the plan selection and usage for model training in the presence of avoidance sectors. BioMed Central 2022-12-06 /pmc/articles/PMC9724419/ /pubmed/36474297 http://dx.doi.org/10.1186/s13014-022-02172-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Fogliata, Antonella
Parabicoli, Sara
Paganini, Lucia
Reggiori, Giacomo
Lobefalo, Francesca
Cozzi, Luca
Franzese, Ciro
Franceschini, Davide
Spoto, Ruggero
Scorsetti, Marta
Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors
title Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors
title_full Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors
title_fullStr Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors
title_full_unstemmed Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors
title_short Knowledge-based DVH estimation and optimization for breast VMAT plans with and without avoidance sectors
title_sort knowledge-based dvh estimation and optimization for breast vmat plans with and without avoidance sectors
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9724419/
https://www.ncbi.nlm.nih.gov/pubmed/36474297
http://dx.doi.org/10.1186/s13014-022-02172-6
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