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Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images
BACKGROUND: Radiation dose to specific cardiac substructures can have a significant on treatment related morbidity and mortality, yet definition of these structures is labor intensive and not standard. Autosegmentation software may potentially address these issues, however it is unclear whether this...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Baishideng Publishing Group Inc
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7918522/ https://www.ncbi.nlm.nih.gov/pubmed/33680876 http://dx.doi.org/10.5306/wjco.v12.i2.95 |
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author | Farrugia, Mark Yu, Han Singh, Anurag K Malhotra, Harish |
author_facet | Farrugia, Mark Yu, Han Singh, Anurag K Malhotra, Harish |
author_sort | Farrugia, Mark |
collection | PubMed |
description | BACKGROUND: Radiation dose to specific cardiac substructures can have a significant on treatment related morbidity and mortality, yet definition of these structures is labor intensive and not standard. Autosegmentation software may potentially address these issues, however it is unclear whether this approach can be broadly applied across different treatment planning conditions. We investigated the feasibility of autosegmentation of the cardiac substructures in four-dimensional (4D) computed tomography (CT), respiratory-gated, non-contrasted imaging. AIM: To determine whether autosegmentation can be successfully employed on 4DCT respiratory-gated, non-contrasted imaging. METHODS: We included patients who underwent stereotactic body radiation therapy for inoperable, early-stage non-small cell lung cancer from 2007 to 2019. All patients were simulated via 4DCT imaging with respiratory gating without intravenous contrast. Generated structure quality was evaluated by degree of required manual edits and volume discrepancy between the autocontoured structures and its edited sister structure. RESULTS: Initial 17-structure cardiac atlas was generated with 20 patients followed by three successive iterations of 10 patients using MIM software. The great vessels and heart chambers were reliably autosegmented with most edits considered minor. In contrast, coronary arteries either failed to be autosegmented or the generated structures required major alterations necessitating deletion and manual definition. Similarly, the generated mitral and tricuspid valves were poor whereas the aortic and pulmonary valves required at least minor and moderate changes respectively. For the majority of subsites, the additional samples did not appear to substantially impact the quality of generated structures. Volumetric analysis between autosegmented and its manually edited sister structure yielded comparable findings to the physician-based assessment of structure quality. CONCLUSION: The use of MIM software with 30-sample subject library was found to be useful in delineating many of the heart substructures with acceptable clinical accuracy on respiratory-gated 4DCT imaging. Small volume structures, such as the coronary arteries were poorly autosegmented and require manual definition. |
format | Online Article Text |
id | pubmed-7918522 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Baishideng Publishing Group Inc |
record_format | MEDLINE/PubMed |
spelling | pubmed-79185222021-03-04 Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images Farrugia, Mark Yu, Han Singh, Anurag K Malhotra, Harish World J Clin Oncol Basic Study BACKGROUND: Radiation dose to specific cardiac substructures can have a significant on treatment related morbidity and mortality, yet definition of these structures is labor intensive and not standard. Autosegmentation software may potentially address these issues, however it is unclear whether this approach can be broadly applied across different treatment planning conditions. We investigated the feasibility of autosegmentation of the cardiac substructures in four-dimensional (4D) computed tomography (CT), respiratory-gated, non-contrasted imaging. AIM: To determine whether autosegmentation can be successfully employed on 4DCT respiratory-gated, non-contrasted imaging. METHODS: We included patients who underwent stereotactic body radiation therapy for inoperable, early-stage non-small cell lung cancer from 2007 to 2019. All patients were simulated via 4DCT imaging with respiratory gating without intravenous contrast. Generated structure quality was evaluated by degree of required manual edits and volume discrepancy between the autocontoured structures and its edited sister structure. RESULTS: Initial 17-structure cardiac atlas was generated with 20 patients followed by three successive iterations of 10 patients using MIM software. The great vessels and heart chambers were reliably autosegmented with most edits considered minor. In contrast, coronary arteries either failed to be autosegmented or the generated structures required major alterations necessitating deletion and manual definition. Similarly, the generated mitral and tricuspid valves were poor whereas the aortic and pulmonary valves required at least minor and moderate changes respectively. For the majority of subsites, the additional samples did not appear to substantially impact the quality of generated structures. Volumetric analysis between autosegmented and its manually edited sister structure yielded comparable findings to the physician-based assessment of structure quality. CONCLUSION: The use of MIM software with 30-sample subject library was found to be useful in delineating many of the heart substructures with acceptable clinical accuracy on respiratory-gated 4DCT imaging. Small volume structures, such as the coronary arteries were poorly autosegmented and require manual definition. Baishideng Publishing Group Inc 2021-02-24 2021-02-24 /pmc/articles/PMC7918522/ /pubmed/33680876 http://dx.doi.org/10.5306/wjco.v12.i2.95 Text en ©The Author(s) 2020. Published by Baishideng Publishing Group Inc. All rights reserved. http://creativecommons.org/licenses/by-nc/4.0/ This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. |
spellingShingle | Basic Study Farrugia, Mark Yu, Han Singh, Anurag K Malhotra, Harish Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images |
title | Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images |
title_full | Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images |
title_fullStr | Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images |
title_full_unstemmed | Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images |
title_short | Autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images |
title_sort | autosegmentation of cardiac substructures in respiratory-gated, non-contrasted computed tomography images |
topic | Basic Study |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7918522/ https://www.ncbi.nlm.nih.gov/pubmed/33680876 http://dx.doi.org/10.5306/wjco.v12.i2.95 |
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