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Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation

Radiologists have used margin characteristics based on routine visual analysis; however, the attenuation changes at the margin of the lesion on CT images have not been quantitatively assessed. We established a CT-based margin analysis method by comparing a target lesion with normal lung attenuation,...

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Autores principales: Koo, Hyun Jung, Kim, Mi Young, Koo, Ja Hwan, Sung, Yu Sub, Jung, Jiwon, Kim, Sung-Han, Choi, Chang-Min, Kim, Hwa Jung
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436675/
https://www.ncbi.nlm.nih.gov/pubmed/28545080
http://dx.doi.org/10.1371/journal.pone.0177379
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author Koo, Hyun Jung
Kim, Mi Young
Koo, Ja Hwan
Sung, Yu Sub
Jung, Jiwon
Kim, Sung-Han
Choi, Chang-Min
Kim, Hwa Jung
author_facet Koo, Hyun Jung
Kim, Mi Young
Koo, Ja Hwan
Sung, Yu Sub
Jung, Jiwon
Kim, Sung-Han
Choi, Chang-Min
Kim, Hwa Jung
author_sort Koo, Hyun Jung
collection PubMed
description Radiologists have used margin characteristics based on routine visual analysis; however, the attenuation changes at the margin of the lesion on CT images have not been quantitatively assessed. We established a CT-based margin analysis method by comparing a target lesion with normal lung attenuation, drawing a slope to represent the attenuation changes. This approach was applied to patients with invasive mucinous adenocarcinoma (n = 40) or bacterial pneumonia (n = 30). Correlations among multiple regions of interest (ROIs) were obtained using intraclass correlation coefficient (ICC) values. CT visual assessment, margin and texture parameters were compared for differentiating the two disease entities. The attenuation and margin parameters in multiple ROIs showed excellent ICC values. Attenuation slopes obtained at the margins revealed a difference between invasive mucinous adenocarcinoma and pneumonia (P<0.001), and mucinous adenocarcinoma produced a sharply declining attenuation slope. On multivariable logistic regression analysis, pneumonia had an ill-defined margin (odds ratio (OR), 4.84; 95% confidence interval (CI), 1.26–18.52; P = 0.02), ground-glass opacity (OR, 8.55; 95% CI, 2.09–34.95; P = 0.003), and gradually declining attenuation at the margin (OR, 12.63; 95% CI, 2.77–57.51, P = 0.001). CT-based margin analysis method has a potential to act as an imaging parameter for differentiating invasive mucinous adenocarcinoma and bacterial pneumonia.
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spelling pubmed-54366752017-05-27 Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation Koo, Hyun Jung Kim, Mi Young Koo, Ja Hwan Sung, Yu Sub Jung, Jiwon Kim, Sung-Han Choi, Chang-Min Kim, Hwa Jung PLoS One Research Article Radiologists have used margin characteristics based on routine visual analysis; however, the attenuation changes at the margin of the lesion on CT images have not been quantitatively assessed. We established a CT-based margin analysis method by comparing a target lesion with normal lung attenuation, drawing a slope to represent the attenuation changes. This approach was applied to patients with invasive mucinous adenocarcinoma (n = 40) or bacterial pneumonia (n = 30). Correlations among multiple regions of interest (ROIs) were obtained using intraclass correlation coefficient (ICC) values. CT visual assessment, margin and texture parameters were compared for differentiating the two disease entities. The attenuation and margin parameters in multiple ROIs showed excellent ICC values. Attenuation slopes obtained at the margins revealed a difference between invasive mucinous adenocarcinoma and pneumonia (P<0.001), and mucinous adenocarcinoma produced a sharply declining attenuation slope. On multivariable logistic regression analysis, pneumonia had an ill-defined margin (odds ratio (OR), 4.84; 95% confidence interval (CI), 1.26–18.52; P = 0.02), ground-glass opacity (OR, 8.55; 95% CI, 2.09–34.95; P = 0.003), and gradually declining attenuation at the margin (OR, 12.63; 95% CI, 2.77–57.51, P = 0.001). CT-based margin analysis method has a potential to act as an imaging parameter for differentiating invasive mucinous adenocarcinoma and bacterial pneumonia. Public Library of Science 2017-05-18 /pmc/articles/PMC5436675/ /pubmed/28545080 http://dx.doi.org/10.1371/journal.pone.0177379 Text en © 2017 Koo 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
Koo, Hyun Jung
Kim, Mi Young
Koo, Ja Hwan
Sung, Yu Sub
Jung, Jiwon
Kim, Sung-Han
Choi, Chang-Min
Kim, Hwa Jung
Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation
title Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation
title_full Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation
title_fullStr Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation
title_full_unstemmed Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation
title_short Computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation
title_sort computerized margin and texture analyses for differentiating bacterial pneumonia and invasive mucinous adenocarcinoma presenting as consolidation
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436675/
https://www.ncbi.nlm.nih.gov/pubmed/28545080
http://dx.doi.org/10.1371/journal.pone.0177379
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