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Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine

Background: Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive mod...

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Autores principales: Moreau, Noémie, Bonnor, Laurine, Jaudet, Cyril, Lechippey, Laetitia, Falzone, Nadia, Batalla, Alain, Bertaut, Cindy, Corroyer-Dulmont, Aurélien
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001389/
https://www.ncbi.nlm.nih.gov/pubmed/36900087
http://dx.doi.org/10.3390/diagnostics13050943
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author Moreau, Noémie
Bonnor, Laurine
Jaudet, Cyril
Lechippey, Laetitia
Falzone, Nadia
Batalla, Alain
Bertaut, Cindy
Corroyer-Dulmont, Aurélien
author_facet Moreau, Noémie
Bonnor, Laurine
Jaudet, Cyril
Lechippey, Laetitia
Falzone, Nadia
Batalla, Alain
Bertaut, Cindy
Corroyer-Dulmont, Aurélien
author_sort Moreau, Noémie
collection PubMed
description Background: Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload. Methods. Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance. Results. For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%. Conclusions. The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time.
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spelling pubmed-100013892023-03-11 Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine Moreau, Noémie Bonnor, Laurine Jaudet, Cyril Lechippey, Laetitia Falzone, Nadia Batalla, Alain Bertaut, Cindy Corroyer-Dulmont, Aurélien Diagnostics (Basel) Article Background: Arc therapy allows for better dose deposition conformation, but the radiotherapy plans (RT plans) are more complex, requiring patient-specific pre-treatment quality assurance (QA). In turn, pre-treatment QA adds to the workload. The objective of this study was to develop a predictive model of Delta4-QA results based on RT-plan complexity indices to reduce QA workload. Methods. Six complexity indices were extracted from 1632 RT VMAT plans. A machine learning (ML) model was developed for classification purpose (two classes: compliance with the QA plan or not). For more complex locations (breast, pelvis and head and neck), innovative deep hybrid learning (DHL) was trained to achieve better performance. Results. For not complex RT plans (with brain and thorax tumor locations), the ML model achieved 100% specificity and 98.9% sensitivity. However, for more complex RT plans, specificity falls to 87%. For these complex RT plans, an innovative QA classification method using DHL was developed and achieved a sensitivity of 100% and a specificity of 97.72%. Conclusions. The ML and DHL models predicted QA results with a high degree of accuracy. Our predictive QA online platform is offering substantial time savings in terms of accelerator occupancy and working time. MDPI 2023-03-02 /pmc/articles/PMC10001389/ /pubmed/36900087 http://dx.doi.org/10.3390/diagnostics13050943 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Moreau, Noémie
Bonnor, Laurine
Jaudet, Cyril
Lechippey, Laetitia
Falzone, Nadia
Batalla, Alain
Bertaut, Cindy
Corroyer-Dulmont, Aurélien
Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine
title Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine
title_full Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine
title_fullStr Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine
title_full_unstemmed Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine
title_short Deep Hybrid Learning Prediction of Patient-Specific Quality Assurance in Radiotherapy: Implementation in Clinical Routine
title_sort deep hybrid learning prediction of patient-specific quality assurance in radiotherapy: implementation in clinical routine
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001389/
https://www.ncbi.nlm.nih.gov/pubmed/36900087
http://dx.doi.org/10.3390/diagnostics13050943
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