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Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients
BACKGROUND: Four-dimensional computed tomography (4D-CT) ventilation is an emerging imaging modality. Functional avoidance of regions according to 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated associations between 4D-CT ventilation-based dosimetric paramete...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169903/ https://www.ncbi.nlm.nih.gov/pubmed/30281625 http://dx.doi.org/10.1371/journal.pone.0204721 |
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author | Otsuka, Masakazu Monzen, Hajime Matsumoto, Kenji Tamura, Mikoto Inada, Masahiro Kadoya, Noriyuki Nishimura, Yasumasa |
author_facet | Otsuka, Masakazu Monzen, Hajime Matsumoto, Kenji Tamura, Mikoto Inada, Masahiro Kadoya, Noriyuki Nishimura, Yasumasa |
author_sort | Otsuka, Masakazu |
collection | PubMed |
description | BACKGROUND: Four-dimensional computed tomography (4D-CT) ventilation is an emerging imaging modality. Functional avoidance of regions according to 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated associations between 4D-CT ventilation-based dosimetric parameters and clinical outcomes. METHODS: Pre-treatment 4D-CT data were used to retrospectively construct ventilation images for 40 thoracic cancer patients retrospectively. Fifteen patients were treated with conventional radiation therapy, 6 patients with hyperfractionated radiation therapy and 19 patients with stereotactic body radiation therapy (SBRT). Ventilation images were calculated from 4D-CT data using a deformable image registration and Jacobian-based algorithm. Each ventilation map was normalized by converting it to percentile images. Ventilation-based dosimetric parameters (Mean Dose, V5 [percent lung volume receiving ≥5 Gy], and V20 [percent lung volume receiving ≥20 Gy]) were calculated for highly and poorly ventilated regions. To test whether the ventilation-based dosimetric parameters could be used predict radiation pneumonitis of ≥Grade 2, the area under the curve (AUC) was determined from the receiver operating characteristic analysis. RESULTS: For Mean Dose, poorly ventilated lung regions in the 0–30% range showed the highest AUC value (0.809; 95% confidence interval [CI], 0.663–0.955). For V20, poorly ventilated lung regions in the 0–20% range had the highest AUC value (0.774; 95% [CI], 0.598–0.915), and for V5, poorly ventilated lung regions in the 0–30% range had the highest AUC value (0.843; 95% [CI], 0.732–0.954). The highest AUC values for Mean Dose, V20, and V5 were obtained in poorly ventilated regions. There were significant differences in all dosimetric parameters between radiation pneumonitis of Grade 1 and Grade ≥2. CONCLUSIONS: Poorly ventilated lung regions identified on 4D-CT had higher AUC values than highly ventilated regions, suggesting that functional planning based on poorly ventilated regions may reduce the risk of lung toxicity in radiation therapy. |
format | Online Article Text |
id | pubmed-6169903 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-61699032018-10-19 Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients Otsuka, Masakazu Monzen, Hajime Matsumoto, Kenji Tamura, Mikoto Inada, Masahiro Kadoya, Noriyuki Nishimura, Yasumasa PLoS One Research Article BACKGROUND: Four-dimensional computed tomography (4D-CT) ventilation is an emerging imaging modality. Functional avoidance of regions according to 4D-CT ventilation may reduce lung toxicity after radiation therapy. This study evaluated associations between 4D-CT ventilation-based dosimetric parameters and clinical outcomes. METHODS: Pre-treatment 4D-CT data were used to retrospectively construct ventilation images for 40 thoracic cancer patients retrospectively. Fifteen patients were treated with conventional radiation therapy, 6 patients with hyperfractionated radiation therapy and 19 patients with stereotactic body radiation therapy (SBRT). Ventilation images were calculated from 4D-CT data using a deformable image registration and Jacobian-based algorithm. Each ventilation map was normalized by converting it to percentile images. Ventilation-based dosimetric parameters (Mean Dose, V5 [percent lung volume receiving ≥5 Gy], and V20 [percent lung volume receiving ≥20 Gy]) were calculated for highly and poorly ventilated regions. To test whether the ventilation-based dosimetric parameters could be used predict radiation pneumonitis of ≥Grade 2, the area under the curve (AUC) was determined from the receiver operating characteristic analysis. RESULTS: For Mean Dose, poorly ventilated lung regions in the 0–30% range showed the highest AUC value (0.809; 95% confidence interval [CI], 0.663–0.955). For V20, poorly ventilated lung regions in the 0–20% range had the highest AUC value (0.774; 95% [CI], 0.598–0.915), and for V5, poorly ventilated lung regions in the 0–30% range had the highest AUC value (0.843; 95% [CI], 0.732–0.954). The highest AUC values for Mean Dose, V20, and V5 were obtained in poorly ventilated regions. There were significant differences in all dosimetric parameters between radiation pneumonitis of Grade 1 and Grade ≥2. CONCLUSIONS: Poorly ventilated lung regions identified on 4D-CT had higher AUC values than highly ventilated regions, suggesting that functional planning based on poorly ventilated regions may reduce the risk of lung toxicity in radiation therapy. Public Library of Science 2018-10-03 /pmc/articles/PMC6169903/ /pubmed/30281625 http://dx.doi.org/10.1371/journal.pone.0204721 Text en © 2018 Otsuka et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Otsuka, Masakazu Monzen, Hajime Matsumoto, Kenji Tamura, Mikoto Inada, Masahiro Kadoya, Noriyuki Nishimura, Yasumasa Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients |
title | Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients |
title_full | Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients |
title_fullStr | Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients |
title_full_unstemmed | Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients |
title_short | Evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients |
title_sort | evaluation of lung toxicity risk with computed tomography ventilation image for thoracic cancer patients |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6169903/ https://www.ncbi.nlm.nih.gov/pubmed/30281625 http://dx.doi.org/10.1371/journal.pone.0204721 |
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