CT-Based Local Distribution Metric Improves Characterization of COPD

Abstract Parametric response mapping (PRM) of paired CT lung images has been shown to improve the phenotyping of COPD by allowing for the visualization and quantification of non-emphysematous air trapping component, referred to as functional small airways disease (fSAD). Although promising, large va...

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Autores principales:	Benjamin A. Hoff, Esther Pompe, Stefanie Galbán, Dirkje S. Postma, Jan-Willem J. Lammers, Nick H. T. ten Hacken, Leo Koenderman, Timothy D. Johnson, Stijn E. Verleden, Pim A. de Jong, Firdaus A. A. Mohamed Hoesein, Maarten van den Berge, Brian D. Ross, Craig J. Galbán
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2017
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9785d98cf36c4301add005d9784b2033
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spelling	oai:doaj.org-article:9785d98cf36c4301add005d9784b20332021-12-02T15:06:14ZCT-Based Local Distribution Metric Improves Characterization of COPD10.1038/s41598-017-02871-12045-2322https://doaj.org/article/9785d98cf36c4301add005d9784b20332017-06-01T00:00:00Zhttps://doi.org/10.1038/s41598-017-02871-1https://doaj.org/toc/2045-2322Abstract Parametric response mapping (PRM) of paired CT lung images has been shown to improve the phenotyping of COPD by allowing for the visualization and quantification of non-emphysematous air trapping component, referred to as functional small airways disease (fSAD). Although promising, large variability in the standard method for analyzing PRMfSAD has been observed. We postulate that representing the 3D PRMfSAD data as a single scalar quantity (relative volume of PRMfSAD) oversimplifies the original 3D data, limiting its potential to detect the subtle progression of COPD as well as varying subtypes. In this study, we propose a new approach to analyze PRM. Based on topological techniques, we generate 3D maps of local topological features from 3D PRMfSAD classification maps. We found that the surface area of fSAD (SfSAD) was the most robust and significant independent indicator of clinically meaningful measures of COPD. We also confirmed by micro-CT of human lung specimens that structural differences are associated with unique SfSAD patterns, and demonstrated longitudinal feature alterations occurred with worsening pulmonary function independent of an increase in disease extent. These findings suggest that our technique captures additional COPD characteristics, which may provide important opportunities for improved diagnosis of COPD patients.Benjamin A. HoffEsther PompeStefanie GalbánDirkje S. PostmaJan-Willem J. LammersNick H. T. ten HackenLeo KoendermanTimothy D. JohnsonStijn E. VerledenPim A. de JongFirdaus A. A. Mohamed HoeseinMaarten van den BergeBrian D. RossCraig J. GalbánNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 7, Iss 1, Pp 1-12 (2017)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Benjamin A. Hoff Esther Pompe Stefanie Galbán Dirkje S. Postma Jan-Willem J. Lammers Nick H. T. ten Hacken Leo Koenderman Timothy D. Johnson Stijn E. Verleden Pim A. de Jong Firdaus A. A. Mohamed Hoesein Maarten van den Berge Brian D. Ross Craig J. Galbán CT-Based Local Distribution Metric Improves Characterization of COPD
description	Abstract Parametric response mapping (PRM) of paired CT lung images has been shown to improve the phenotyping of COPD by allowing for the visualization and quantification of non-emphysematous air trapping component, referred to as functional small airways disease (fSAD). Although promising, large variability in the standard method for analyzing PRMfSAD has been observed. We postulate that representing the 3D PRMfSAD data as a single scalar quantity (relative volume of PRMfSAD) oversimplifies the original 3D data, limiting its potential to detect the subtle progression of COPD as well as varying subtypes. In this study, we propose a new approach to analyze PRM. Based on topological techniques, we generate 3D maps of local topological features from 3D PRMfSAD classification maps. We found that the surface area of fSAD (SfSAD) was the most robust and significant independent indicator of clinically meaningful measures of COPD. We also confirmed by micro-CT of human lung specimens that structural differences are associated with unique SfSAD patterns, and demonstrated longitudinal feature alterations occurred with worsening pulmonary function independent of an increase in disease extent. These findings suggest that our technique captures additional COPD characteristics, which may provide important opportunities for improved diagnosis of COPD patients.
format	article
author	Benjamin A. Hoff Esther Pompe Stefanie Galbán Dirkje S. Postma Jan-Willem J. Lammers Nick H. T. ten Hacken Leo Koenderman Timothy D. Johnson Stijn E. Verleden Pim A. de Jong Firdaus A. A. Mohamed Hoesein Maarten van den Berge Brian D. Ross Craig J. Galbán
author_facet	Benjamin A. Hoff Esther Pompe Stefanie Galbán Dirkje S. Postma Jan-Willem J. Lammers Nick H. T. ten Hacken Leo Koenderman Timothy D. Johnson Stijn E. Verleden Pim A. de Jong Firdaus A. A. Mohamed Hoesein Maarten van den Berge Brian D. Ross Craig J. Galbán
author_sort	Benjamin A. Hoff
title	CT-Based Local Distribution Metric Improves Characterization of COPD
title_short	CT-Based Local Distribution Metric Improves Characterization of COPD
title_full	CT-Based Local Distribution Metric Improves Characterization of COPD
title_fullStr	CT-Based Local Distribution Metric Improves Characterization of COPD
title_full_unstemmed	CT-Based Local Distribution Metric Improves Characterization of COPD
title_sort	ct-based local distribution metric improves characterization of copd
publisher	Nature Portfolio
publishDate	2017
url	https://doaj.org/article/9785d98cf36c4301add005d9784b2033
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_version_	1718388530055479296

CT-Based Local Distribution Metric Improves Characterization of COPD

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