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Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy

Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume whil...

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Autores principales: Momin, Shadab, Wolf, Jonathan, Roper, Justin, Lei, Yang, Liu, Tian, Bradley, Jeffrey D., Higgins, Kristin, Yang, Xiaofeng, Zhang, Jiahan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755869/
https://www.ncbi.nlm.nih.gov/pubmed/36531046
http://dx.doi.org/10.3389/fonc.2022.1055428
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author Momin, Shadab
Wolf, Jonathan
Roper, Justin
Lei, Yang
Liu, Tian
Bradley, Jeffrey D.
Higgins, Kristin
Yang, Xiaofeng
Zhang, Jiahan
author_facet Momin, Shadab
Wolf, Jonathan
Roper, Justin
Lei, Yang
Liu, Tian
Bradley, Jeffrey D.
Higgins, Kristin
Yang, Xiaofeng
Zhang, Jiahan
author_sort Momin, Shadab
collection PubMed
description Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume while reducing cardiac substructure doses. However, it is often challenging to design such treatment plans due to complex tradeoffs involving numerous cardiac substructures. Here, we built a cardiac-substructure-based knowledge-based planning (CS-KBP) model and retrospectively evaluated its performance against a cardiac-based KBP (C-KBP) model and manually optimized patient treatment plans. CS-KBP/C-KBP models were built with 27 previously-treated plans that preferentially spare the heart. While the C-KBP training plans were created with whole heart structures, the CS-KBP model training plans each have 15 cardiac substructures (coronary arteries, valves, great vessels, and chambers of the heart). CS-KBP training plans reflect cardiac-substructure sparing preferences. We evaluated both models on 28 additional patients. Three sets of treatment plans were compared: (1) manually optimized, (2) C-KBP model-generated, and (3) CS-KBP model-generated. Plans were normalized to receive the prescribed dose to at least 95% of the PTV. A two-tailed paired-sample t-test was performed for clinically relevant dose-volume metrics to evaluate the performance of the CS-KBP model against the C-KBP model and clinical plans, respectively. Overall results show significantly improved cardiac substructure sparing by CS-KBP in comparison to C-KBP and the clinical plans. For instance, the average left anterior descending artery volume receiving 15 Gy (V15 Gy) was significantly lower (p < 0.01) for CS-KBP (0.69 ± 1.57 cc) compared to the clinical plans (1.23 ± 1.76 cc) and C-KBP plans (1.05 ± 1.68 cc). In conclusion, the CS-KBP model significantly improved cardiac-substructure sparing without exceeding the tolerances of other OARs or compromising PTV coverage.
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spelling pubmed-97558692022-12-17 Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy Momin, Shadab Wolf, Jonathan Roper, Justin Lei, Yang Liu, Tian Bradley, Jeffrey D. Higgins, Kristin Yang, Xiaofeng Zhang, Jiahan Front Oncol Oncology Radiotherapy (RT) doses to cardiac substructures from the definitive treatment of locally advanced non-small cell lung cancers (NSCLC) have been linked to post-RT cardiac toxicities. With modern treatment delivery techniques, it is possible to focus radiation doses to the planning target volume while reducing cardiac substructure doses. However, it is often challenging to design such treatment plans due to complex tradeoffs involving numerous cardiac substructures. Here, we built a cardiac-substructure-based knowledge-based planning (CS-KBP) model and retrospectively evaluated its performance against a cardiac-based KBP (C-KBP) model and manually optimized patient treatment plans. CS-KBP/C-KBP models were built with 27 previously-treated plans that preferentially spare the heart. While the C-KBP training plans were created with whole heart structures, the CS-KBP model training plans each have 15 cardiac substructures (coronary arteries, valves, great vessels, and chambers of the heart). CS-KBP training plans reflect cardiac-substructure sparing preferences. We evaluated both models on 28 additional patients. Three sets of treatment plans were compared: (1) manually optimized, (2) C-KBP model-generated, and (3) CS-KBP model-generated. Plans were normalized to receive the prescribed dose to at least 95% of the PTV. A two-tailed paired-sample t-test was performed for clinically relevant dose-volume metrics to evaluate the performance of the CS-KBP model against the C-KBP model and clinical plans, respectively. Overall results show significantly improved cardiac substructure sparing by CS-KBP in comparison to C-KBP and the clinical plans. For instance, the average left anterior descending artery volume receiving 15 Gy (V15 Gy) was significantly lower (p < 0.01) for CS-KBP (0.69 ± 1.57 cc) compared to the clinical plans (1.23 ± 1.76 cc) and C-KBP plans (1.05 ± 1.68 cc). In conclusion, the CS-KBP model significantly improved cardiac-substructure sparing without exceeding the tolerances of other OARs or compromising PTV coverage. Frontiers Media S.A. 2022-12-02 /pmc/articles/PMC9755869/ /pubmed/36531046 http://dx.doi.org/10.3389/fonc.2022.1055428 Text en Copyright © 2022 Momin, Wolf, Roper, Lei, Liu, Bradley, Higgins, Yang and Zhang https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Oncology
Momin, Shadab
Wolf, Jonathan
Roper, Justin
Lei, Yang
Liu, Tian
Bradley, Jeffrey D.
Higgins, Kristin
Yang, Xiaofeng
Zhang, Jiahan
Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy
title Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy
title_full Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy
title_fullStr Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy
title_full_unstemmed Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy
title_short Enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy
title_sort enhanced cardiac substructure sparing through knowledge-based treatment planning for non-small cell lung cancer radiotherapy
topic Oncology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9755869/
https://www.ncbi.nlm.nih.gov/pubmed/36531046
http://dx.doi.org/10.3389/fonc.2022.1055428
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