Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer

Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners were allowed to manually adapt them....

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Detalles Bibliográficos
Autores principales: Bakx, Nienke, van der Sangen, Maurice, Theuws, Jacqueline, Bluemink, Johanna, Hurkmans, Coen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Original Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544072/
https://www.ncbi.nlm.nih.gov/pubmed/37789873
http://dx.doi.org/10.1016/j.phro.2023.100496
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author Bakx, Nienke
van der Sangen, Maurice
Theuws, Jacqueline
Bluemink, Johanna
Hurkmans, Coen
author_facet Bakx, Nienke
van der Sangen, Maurice
Theuws, Jacqueline
Bluemink, Johanna
Hurkmans, Coen
author_sort Bakx, Nienke
collection PubMed
description Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners were allowed to manually adapt them. Plans were evaluated based on clinical goals and DVH parameters. Thirty-seven of 50plans did fulfill all clinical goals without adjustments. Thirteen of these 37 plans were still adjusted but did not improve mean heart or lung dose. These results leave room for improvement of both the DL model as well as education on clinically relevant adjustments.
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spelling pubmed-105440722023-10-03 Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer Bakx, Nienke van der Sangen, Maurice Theuws, Jacqueline Bluemink, Johanna Hurkmans, Coen Phys Imaging Radiat Oncol Original Research Article Deep learning (DL) models are increasingly studied to automate the process of radiotherapy treatment planning. This study evaluates the clinical use of such a model for whole breast radiotherapy. Treatment plans were automatically generated, after which planners were allowed to manually adapt them. Plans were evaluated based on clinical goals and DVH parameters. Thirty-seven of 50plans did fulfill all clinical goals without adjustments. Thirteen of these 37 plans were still adjusted but did not improve mean heart or lung dose. These results leave room for improvement of both the DL model as well as education on clinically relevant adjustments. Elsevier 2023-09-27 /pmc/articles/PMC10544072/ /pubmed/37789873 http://dx.doi.org/10.1016/j.phro.2023.100496 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Original Research Article
Bakx, Nienke
van der Sangen, Maurice
Theuws, Jacqueline
Bluemink, Johanna
Hurkmans, Coen
Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer
title Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer
title_full Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer
title_fullStr Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer
title_full_unstemmed Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer
title_short Evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer
title_sort evaluation of a clinically introduced deep learning model for radiotherapy treatment planning of breast cancer
topic Original Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544072/
https://www.ncbi.nlm.nih.gov/pubmed/37789873
http://dx.doi.org/10.1016/j.phro.2023.100496
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