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Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction
Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monito...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475190/ https://www.ncbi.nlm.nih.gov/pubmed/37660057 http://dx.doi.org/10.1186/s13014-023-02340-2 |
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author | Liu, Hefei Schaal, David Curry, Heather Clark, Ryan Magliari, Anthony Kupelian, Patrick Khuntia, Deepak Beriwal, Sushil |
author_facet | Liu, Hefei Schaal, David Curry, Heather Clark, Ryan Magliari, Anthony Kupelian, Patrick Khuntia, Deepak Beriwal, Sushil |
author_sort | Liu, Hefei |
collection | PubMed |
description | Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monitor inter- or intra-fraction anatomical variations of the target and organs-at-risk (OARs). Ethos™ (Varian Medical Systems, Palo Alto, CA), a cone beam computed tomography (CBCT) based radiotherapy treatment system that uses artificial intelligence (AI) and machine learning to perform ART, was introduced in 2020. Since then, numerous studies have been done to examine the potential benefits of Ethos™ CBCT-guided ART compared to non-adaptive radiotherapy. This review will explore the current trends of Ethos™, including improved CBCT image quality, a feasible clinical workflow, daily automated contouring and treatment planning, and motion management. Nevertheless, evidence of clinical improvements with the use of Ethos™ are limited and is currently under investigation via clinical trials. |
format | Online Article Text |
id | pubmed-10475190 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-104751902023-09-04 Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction Liu, Hefei Schaal, David Curry, Heather Clark, Ryan Magliari, Anthony Kupelian, Patrick Khuntia, Deepak Beriwal, Sushil Radiat Oncol Review Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monitor inter- or intra-fraction anatomical variations of the target and organs-at-risk (OARs). Ethos™ (Varian Medical Systems, Palo Alto, CA), a cone beam computed tomography (CBCT) based radiotherapy treatment system that uses artificial intelligence (AI) and machine learning to perform ART, was introduced in 2020. Since then, numerous studies have been done to examine the potential benefits of Ethos™ CBCT-guided ART compared to non-adaptive radiotherapy. This review will explore the current trends of Ethos™, including improved CBCT image quality, a feasible clinical workflow, daily automated contouring and treatment planning, and motion management. Nevertheless, evidence of clinical improvements with the use of Ethos™ are limited and is currently under investigation via clinical trials. BioMed Central 2023-09-02 /pmc/articles/PMC10475190/ /pubmed/37660057 http://dx.doi.org/10.1186/s13014-023-02340-2 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 | Review Liu, Hefei Schaal, David Curry, Heather Clark, Ryan Magliari, Anthony Kupelian, Patrick Khuntia, Deepak Beriwal, Sushil Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction |
title | Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction |
title_full | Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction |
title_fullStr | Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction |
title_full_unstemmed | Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction |
title_short | Review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction |
title_sort | review of cone beam computed tomography based online adaptive radiotherapy: current trend and future direction |
topic | Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10475190/ https://www.ncbi.nlm.nih.gov/pubmed/37660057 http://dx.doi.org/10.1186/s13014-023-02340-2 |
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