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Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer

BACKGROUND: PET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing static (3D) and respiratory-gated (4D) PET imaging. METHODS: Twenty-six patients...

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Autores principales: Yip, Stephen, McCall, Keisha, Aristophanous, Michalis, Chen, Aileen B., Aerts, Hugo J. W. L., Berbeco, Ross
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269460/
https://www.ncbi.nlm.nih.gov/pubmed/25517987
http://dx.doi.org/10.1371/journal.pone.0115510
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author Yip, Stephen
McCall, Keisha
Aristophanous, Michalis
Chen, Aileen B.
Aerts, Hugo J. W. L.
Berbeco, Ross
author_facet Yip, Stephen
McCall, Keisha
Aristophanous, Michalis
Chen, Aileen B.
Aerts, Hugo J. W. L.
Berbeco, Ross
author_sort Yip, Stephen
collection PubMed
description BACKGROUND: PET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing static (3D) and respiratory-gated (4D) PET imaging. METHODS: Twenty-six patients (34 lesions) received 3D and 4D [(18)F]FDG-PET scans before the chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Texture features, including Maximal correlation coefficient (MCC), Long run low gray (LRLG), Coarseness, Contrast, and Busyness, were computed within the physician-defined tumor volume. The relative difference (δ(3D-4D)) in each texture between the 3D- and 4D-PET imaging was calculated. Coefficient of variation (CV) was used to determine the variability in the textures between all 4D-PET phases. Correlations between tumor volume, motion amplitude, and δ(3D-4D) were also assessed. RESULTS: 4D-PET increased LRLG ( = 1%–2%, p<0.02), Busyness ( = 7%–19%, p<0.01), and decreased MCC ( = 1%–2%, p<7.5×10(−3)), Coarseness ( = 5%–10%, p<0.05) and Contrast ( = 4%–6%, p>0.08) compared to 3D-PET. Nearly negligible variability was found between the 4D phase bins with CV<5% for MCC, LRLG, and Coarseness. For Contrast and Busyness, moderate variability was found with CV = 9% and 10%, respectively. No strong correlation was found between the tumor volume and δ(3D-4D) for the texture features. Motion amplitude had moderate impact on δ for MCC and Busyness and no impact for LRLG, Coarseness, and Contrast. CONCLUSIONS: Significant differences were found in MCC, LRLG, Coarseness, and Busyness between 3D and 4D PET imaging. The variability between phase bins for MCC, LRLG, and Coarseness was negligible, suggesting that similar quantification can be obtained from all phases. Texture features, blurred out by respiratory motion during 3D-PET acquisition, can be better resolved by 4D-PET imaging. 4D-PET textures may have better prognostic value as they are less susceptible to tumor motion.
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spelling pubmed-42694602014-12-26 Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer Yip, Stephen McCall, Keisha Aristophanous, Michalis Chen, Aileen B. Aerts, Hugo J. W. L. Berbeco, Ross PLoS One Research Article BACKGROUND: PET-based texture features have been used to quantify tumor heterogeneity due to their predictive power in treatment outcome. We investigated the sensitivity of texture features to tumor motion by comparing static (3D) and respiratory-gated (4D) PET imaging. METHODS: Twenty-six patients (34 lesions) received 3D and 4D [(18)F]FDG-PET scans before the chemo-radiotherapy. The acquired 4D data were retrospectively binned into five breathing phases to create the 4D image sequence. Texture features, including Maximal correlation coefficient (MCC), Long run low gray (LRLG), Coarseness, Contrast, and Busyness, were computed within the physician-defined tumor volume. The relative difference (δ(3D-4D)) in each texture between the 3D- and 4D-PET imaging was calculated. Coefficient of variation (CV) was used to determine the variability in the textures between all 4D-PET phases. Correlations between tumor volume, motion amplitude, and δ(3D-4D) were also assessed. RESULTS: 4D-PET increased LRLG ( = 1%–2%, p<0.02), Busyness ( = 7%–19%, p<0.01), and decreased MCC ( = 1%–2%, p<7.5×10(−3)), Coarseness ( = 5%–10%, p<0.05) and Contrast ( = 4%–6%, p>0.08) compared to 3D-PET. Nearly negligible variability was found between the 4D phase bins with CV<5% for MCC, LRLG, and Coarseness. For Contrast and Busyness, moderate variability was found with CV = 9% and 10%, respectively. No strong correlation was found between the tumor volume and δ(3D-4D) for the texture features. Motion amplitude had moderate impact on δ for MCC and Busyness and no impact for LRLG, Coarseness, and Contrast. CONCLUSIONS: Significant differences were found in MCC, LRLG, Coarseness, and Busyness between 3D and 4D PET imaging. The variability between phase bins for MCC, LRLG, and Coarseness was negligible, suggesting that similar quantification can be obtained from all phases. Texture features, blurred out by respiratory motion during 3D-PET acquisition, can be better resolved by 4D-PET imaging. 4D-PET textures may have better prognostic value as they are less susceptible to tumor motion. Public Library of Science 2014-12-17 /pmc/articles/PMC4269460/ /pubmed/25517987 http://dx.doi.org/10.1371/journal.pone.0115510 Text en © 2014 Yip 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Yip, Stephen
McCall, Keisha
Aristophanous, Michalis
Chen, Aileen B.
Aerts, Hugo J. W. L.
Berbeco, Ross
Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer
title Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer
title_full Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer
title_fullStr Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer
title_full_unstemmed Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer
title_short Comparison of Texture Features Derived from Static and Respiratory-Gated PET Images in Non-Small Cell Lung Cancer
title_sort comparison of texture features derived from static and respiratory-gated pet images in non-small cell lung cancer
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4269460/
https://www.ncbi.nlm.nih.gov/pubmed/25517987
http://dx.doi.org/10.1371/journal.pone.0115510
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