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Clinical evaluation of two AI models for automated breast cancer plan generation
BACKGROUND: Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817521/ https://www.ncbi.nlm.nih.gov/pubmed/35123517 http://dx.doi.org/10.1186/s13014-022-01993-9 |
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author | Kneepkens, Esther Bakx, Nienke van der Sangen, Maurice Theuws, Jacqueline van der Toorn, Peter-Paul Rijkaart, Dorien van der Leer, Jorien van Nunen, Thérèse Hagelaar, Els Bluemink, Hanneke Hurkmans, Coen |
author_facet | Kneepkens, Esther Bakx, Nienke van der Sangen, Maurice Theuws, Jacqueline van der Toorn, Peter-Paul Rijkaart, Dorien van der Leer, Jorien van Nunen, Thérèse Hagelaar, Els Bluemink, Hanneke Hurkmans, Coen |
author_sort | Kneepkens, Esther |
collection | PubMed |
description | BACKGROUND: Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists. METHODS: For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time. RESULTS: The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90–95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model. CONCLUSIONS: Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90–95% of the AI plans clinically acceptable. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-022-01993-9. |
format | Online Article Text |
id | pubmed-8817521 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-88175212022-02-07 Clinical evaluation of two AI models for automated breast cancer plan generation Kneepkens, Esther Bakx, Nienke van der Sangen, Maurice Theuws, Jacqueline van der Toorn, Peter-Paul Rijkaart, Dorien van der Leer, Jorien van Nunen, Thérèse Hagelaar, Els Bluemink, Hanneke Hurkmans, Coen Radiat Oncol Research BACKGROUND: Artificial intelligence (AI) shows great potential to streamline the treatment planning process. However, its clinical adoption is slow due to the limited number of clinical evaluation studies and because often, the translation of the predicted dose distribution to a deliverable plan is lacking. This study evaluates two different, deliverable AI plans in terms of their clinical acceptability based on quantitative parameters and qualitative evaluation by four radiation oncologists. METHODS: For 20 left-sided node-negative breast cancer patients, treated with a prescribed dose of 40.05 Gy, using tangential beam intensity modulated radiotherapy, two model-based treatment plans were evaluated against the corresponding manual plan. The two models used were an in-house developed U-net model and a vendor-developed contextual atlas regression forest model (cARF). Radiation oncologists evaluated the clinical acceptability of each blinded plan and ranked plans according to preference. Furthermore, a comparison with the manual plan was made based on dose volume histogram parameters, clinical evaluation criteria and preparation time. RESULTS: The U-net model resulted in a higher average and maximum dose to the PTV (median difference 0.37 Gy and 0.47 Gy respectively) and a slightly higher mean heart dose (MHD) (0.01 Gy). The cARF model led to higher average and maximum doses to the PTV (0.30 and 0.39 Gy respectively) and a slightly higher MHD (0.02 Gy) and mean lung dose (MLD, 0.04 Gy). The maximum MHD/MLD difference was ≤ 0.5 Gy for both AI plans. Regardless of these dose differences, 90–95% of the AI plans were considered clinically acceptable versus 90% of the manual plans. Preferences varied between the radiation oncologists. Plan preparation time was comparable between the U-net model and the manual plan (287 s vs 253 s) while the cARF model took longer (471 s). When only considering user interaction, plan generation time was 121 s for the cARF model and 137 s for the U-net model. CONCLUSIONS: Two AI models were used to generate deliverable plans for breast cancer patients, in a time-efficient manner, requiring minimal user interaction. Although the AI plans resulted in slightly higher doses overall, radiation oncologists considered 90–95% of the AI plans clinically acceptable. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13014-022-01993-9. BioMed Central 2022-02-05 /pmc/articles/PMC8817521/ /pubmed/35123517 http://dx.doi.org/10.1186/s13014-022-01993-9 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 Kneepkens, Esther Bakx, Nienke van der Sangen, Maurice Theuws, Jacqueline van der Toorn, Peter-Paul Rijkaart, Dorien van der Leer, Jorien van Nunen, Thérèse Hagelaar, Els Bluemink, Hanneke Hurkmans, Coen Clinical evaluation of two AI models for automated breast cancer plan generation |
title | Clinical evaluation of two AI models for automated breast cancer plan generation |
title_full | Clinical evaluation of two AI models for automated breast cancer plan generation |
title_fullStr | Clinical evaluation of two AI models for automated breast cancer plan generation |
title_full_unstemmed | Clinical evaluation of two AI models for automated breast cancer plan generation |
title_short | Clinical evaluation of two AI models for automated breast cancer plan generation |
title_sort | clinical evaluation of two ai models for automated breast cancer plan generation |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8817521/ https://www.ncbi.nlm.nih.gov/pubmed/35123517 http://dx.doi.org/10.1186/s13014-022-01993-9 |
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