Literature DB >> 32255690

Chronic Obstructive Pulmonary Disease Quantification Using CT Texture Analysis and Densitometry: Results From the Danish Lung Cancer Screening Trial.

Lauge Sørensen1, Mads Nielsen1, Jens Petersen1, Jesper H Pedersen2, Asger Dirksen3, Marleen de Bruijne1,4.   

Abstract

OBJECTIVE. The purpose of this study is to establish whether texture analysis and densitometry are complementary quantitative measures of chronic obstructive pulmonary disease (COPD) in a lung cancer screening setting. MATERIALS AND METHODS. This was a retrospective study of data collected prospectively (in 2004-2010) in the Danish Lung Cancer Screening Trial. The texture score, relative area of emphysema, and percentile density were computed for 1915 baseline low-dose lung CT scans and were evaluated, both individually and in combination, for associations with lung function (i.e., forced expiratory volume in 1 second as a percentage of predicted normal [FEV1% predicted]), diagnosis of mild to severe COPD, and prediction of a rapid decline in lung function. Multivariate linear regression models with lung function as the outcome were compared using the likelihood ratio test or the Vuong test, and AUC values for diagnostic and prognostic capabilities were compared using the DeLong test. RESULTS. Texture showed a significantly stronger association with lung function (p < 0.001 vs densitometric measures), a significantly higher diagnostic AUC value (for COPD, 0.696; for Global Initiative for Chronic Obstructive Lung Disease (GOLD) grade 1, 0.648; for GOLD grade 2, 0.768; and for GOLD grade 3, 0.944; p < 0.001 vs densitometric measures), and a higher but not significantly different association with lung function decline. In addition, only texture could predict a rapid decline in lung function (AUC value, 0.538; p < 0.05 vs random guessing). The combination of texture and both densitometric measures strengthened the association with lung function and decline in lung function (p < 0.001 and p < 0.05, respectively, vs texture) but did not improve diagnostic or prognostic performance. CONCLUSION. The present study highlights texture as a promising quantitative CT measure of COPD to use alongside, or even instead of, densitometric measures. Moreover, texture may allow early detection of COPD in subjects who undergo lung cancer screening.

Entities:  

Keywords:  chronic obstructive pulmonary disease; densitometry; machine learning; quantitative CT; texture analysis

Year:  2020        PMID: 32255690     DOI: 10.2214/AJR.19.22300

Source DB:  PubMed          Journal:  AJR Am J Roentgenol        ISSN: 0361-803X            Impact factor:   3.959


  3 in total

1.  Emphysema Progression at CT by Deep Learning Predicts Functional Impairment and Mortality: Results from the COPDGene Study.

Authors:  Andrea S Oh; David Baraghoshi; David A Lynch; Samuel Y Ash; James D Crapo; Stephen M Humphries
Journal:  Radiology       Date:  2022-05-17       Impact factor: 29.146

2.  Evaluation of COVID-19 chest computed tomography: A texture analysis based on three-dimensional entropy.

Authors:  Andreia S Gaudêncio; Pedro G Vaz; Mirvana Hilal; Guillaume Mahé; Mathieu Lederlin; Anne Humeau-Heurtier; João M Cardoso
Journal:  Biomed Signal Process Control       Date:  2021-04-01       Impact factor: 3.880

3.  Novel Subtypes of Pulmonary Emphysema Based on Spatially-Informed Lung Texture Learning: The Multi-Ethnic Study of Atherosclerosis (MESA) COPD Study.

Authors:  Jie Yang; Elsa D Angelini; Pallavi P Balte; Eric A Hoffman; John H M Austin; Benjamin M Smith; R Graham Barr; Andrew F Laine
Journal:  IEEE Trans Med Imaging       Date:  2021-11-30       Impact factor: 10.048

  3 in total

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