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Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer

PURPOSE: Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-qual...

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Autores principales: Pogue, Joel A., Cardenas, Carlos E., Harms, Joseph, Soike, Michael H., Kole, Adam J., Schneider, Craig S., Veale, Christopher, Popple, Richard, Belliveau, Jean-Guy, McDonald, Andrew M., Stanley, Dennis N.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10344691/
https://www.ncbi.nlm.nih.gov/pubmed/37457825
http://dx.doi.org/10.1016/j.adro.2023.101292
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author Pogue, Joel A.
Cardenas, Carlos E.
Harms, Joseph
Soike, Michael H.
Kole, Adam J.
Schneider, Craig S.
Veale, Christopher
Popple, Richard
Belliveau, Jean-Guy
McDonald, Andrew M.
Stanley, Dennis N.
author_facet Pogue, Joel A.
Cardenas, Carlos E.
Harms, Joseph
Soike, Michael H.
Kole, Adam J.
Schneider, Craig S.
Veale, Christopher
Popple, Richard
Belliveau, Jean-Guy
McDonald, Andrew M.
Stanley, Dennis N.
author_sort Pogue, Joel A.
collection PubMed
description PURPOSE: Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-quality Ethos treatment plans for locally advanced lung cancer. METHODS AND MATERIALS: Fifty patients previously treated with manually generated Eclipse plans for inoperable stage IIIA-IIIC non-small cell lung cancer were included in this institutional review board–approved retrospective study. Fifteen patient plans were used to iteratively optimize a planning template for the Daily Adaptive vs Non-Adaptive External Beam Radiation Therapy With Concurrent Chemotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Prospective Randomized Trial of an Individualized Approach for Toxicity Reduction (ARTIA-Lung); the remaining 35 patients were automatically replanned without intervention. Ethos plan quality was benchmarked against clinical plans and reoptimized knowledge-based RapidPlan (RP) plans, then judged using standard dose-volume histogram metrics, adherence to clinical trial objectives, and qualitative review. RESULTS: Given equal prescription target coverage, Ethos-generated plans showed improved primary and nodal planning target volume V95% coverage (P < .001) and reduced lung gross tumor volume V5 Gy and esophagus D0.03 cc metrics (P ≤ .003) but increased mean esophagus and brachial plexus D0.03 cc metrics (P < .001) compared with RP plans. Eighty percent, 49%, and 51% of Ethos, clinical, and RP plans, respectively, were “per protocol” or met “variation acceptable” ARTIA-Lung planning metrics. Three radiation oncologists qualitatively scored Ethos plans, and 78% of plans were clinically acceptable to all reviewing physicians, with no plans receiving scores requiring major changes. CONCLUSIONS: A standard Ethos template produced lung radiation therapy plans with similar quality to RP plans, elucidating a viable approach for automated plan generation in the Ethos adaptive workspace.
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spelling pubmed-103446912023-07-15 Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer Pogue, Joel A. Cardenas, Carlos E. Harms, Joseph Soike, Michael H. Kole, Adam J. Schneider, Craig S. Veale, Christopher Popple, Richard Belliveau, Jean-Guy McDonald, Andrew M. Stanley, Dennis N. Adv Radiat Oncol Scientific Article PURPOSE: Currently, there is insufficient guidance for standard fractionation lung planning using the Varian Ethos adaptive treatment planning system and its unique intelligent optimization engine. Here, we address this gap in knowledge by developing a methodology to automatically generate high-quality Ethos treatment plans for locally advanced lung cancer. METHODS AND MATERIALS: Fifty patients previously treated with manually generated Eclipse plans for inoperable stage IIIA-IIIC non-small cell lung cancer were included in this institutional review board–approved retrospective study. Fifteen patient plans were used to iteratively optimize a planning template for the Daily Adaptive vs Non-Adaptive External Beam Radiation Therapy With Concurrent Chemotherapy for Locally Advanced Non-Small Cell Lung Cancer: A Prospective Randomized Trial of an Individualized Approach for Toxicity Reduction (ARTIA-Lung); the remaining 35 patients were automatically replanned without intervention. Ethos plan quality was benchmarked against clinical plans and reoptimized knowledge-based RapidPlan (RP) plans, then judged using standard dose-volume histogram metrics, adherence to clinical trial objectives, and qualitative review. RESULTS: Given equal prescription target coverage, Ethos-generated plans showed improved primary and nodal planning target volume V95% coverage (P < .001) and reduced lung gross tumor volume V5 Gy and esophagus D0.03 cc metrics (P ≤ .003) but increased mean esophagus and brachial plexus D0.03 cc metrics (P < .001) compared with RP plans. Eighty percent, 49%, and 51% of Ethos, clinical, and RP plans, respectively, were “per protocol” or met “variation acceptable” ARTIA-Lung planning metrics. Three radiation oncologists qualitatively scored Ethos plans, and 78% of plans were clinically acceptable to all reviewing physicians, with no plans receiving scores requiring major changes. CONCLUSIONS: A standard Ethos template produced lung radiation therapy plans with similar quality to RP plans, elucidating a viable approach for automated plan generation in the Ethos adaptive workspace. Elsevier 2023-06-14 /pmc/articles/PMC10344691/ /pubmed/37457825 http://dx.doi.org/10.1016/j.adro.2023.101292 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 Scientific Article
Pogue, Joel A.
Cardenas, Carlos E.
Harms, Joseph
Soike, Michael H.
Kole, Adam J.
Schneider, Craig S.
Veale, Christopher
Popple, Richard
Belliveau, Jean-Guy
McDonald, Andrew M.
Stanley, Dennis N.
Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer
title Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer
title_full Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer
title_fullStr Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer
title_full_unstemmed Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer
title_short Benchmarking Automated Machine Learning-Enhanced Planning With Ethos Against Manual and Knowledge-Based Planning for Locally Advanced Lung Cancer
title_sort benchmarking automated machine learning-enhanced planning with ethos against manual and knowledge-based planning for locally advanced lung cancer
topic Scientific Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10344691/
https://www.ncbi.nlm.nih.gov/pubmed/37457825
http://dx.doi.org/10.1016/j.adro.2023.101292
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