scholarly journals Quantitative CT Analysis in Patients with Pulmonary Emphysema: Do Calculated Differences Between Full Inspiration and Expiration Correlate with Lung Function?

2020 ◽  
Vol Volume 15 ◽  
pp. 1877-1886
Author(s):  
Lan Song ◽  
Jonas A Leppig ◽  
Ralf H Hubner ◽  
Bianca C Lassen-Schmidt ◽  
Konrad Neumann ◽  
...  
Keyword(s):  
Lung Function ◽  
Pulmonary Emphysema ◽  
Quantitative Ct ◽  
Full Inspiration ◽  
Ct Analysis ◽  
Quantitative Ct Analysis

scholarly journals Quantitative CT analysis in patients with pulmonary emphysema: is lung function influenced by concomitant unspecific pulmonary fibrosis?

2019 ◽  
Vol Volume 14 ◽  
pp. 1583-1593
Author(s):  
Felix W Feldhaus ◽  
Dorothea Cornelia Theilig ◽  
Ralf-Harto Hubner ◽  
Jan-Martin Kuhnigk ◽  
Konrad Neumann ◽  
...  
Keyword(s):  
Lung Function ◽  
Pulmonary Fibrosis ◽  
Pulmonary Emphysema ◽  
Quantitative Ct ◽  
Ct Analysis ◽  
Quantitative Ct Analysis

Quantitative CT analysis for bronchiolitis obliterans in perinatally HIV-infected adolescents—comparison with controls and lung function data

2020 ◽  
Vol 30 (8) ◽  
pp. 4358-4368
Author(s):  
Christian A. Barrera ◽  
Anne-Marie du Plessis ◽  
Hansel J. Otero ◽  
Sana Mahtab ◽  
Leah N. Githinji ◽  
...  
Keyword(s):  
Lung Function ◽  
Bronchiolitis Obliterans ◽  
Quantitative Ct ◽  
Function Data ◽  
Ct Analysis ◽  
Quantitative Ct Analysis

scholarly journals MUC5B variant is associated with visually and quantitatively detected preclinical pulmonary fibrosis

Thorax ◽  
2019 ◽  
Vol 74 (12) ◽  
pp. 1131-1139 ◽  
Author(s):  
Susan K Mathai ◽  
Stephen Humphries ◽  
Jonathan A Kropski ◽  
Timothy S Blackwell ◽  
Julia Powers ◽  
...  
Keyword(s):  
Deep Learning ◽  
Pulmonary Fibrosis ◽  
Learning Algorithm ◽  
Quantitative Ct ◽  
First Degree Relatives ◽  
Deep Learning Algorithm ◽  
Increased Risk ◽  
Ct Evaluation ◽  
Ct Analysis ◽  
Quantitative Ct Analysis

BackgroundRelatives of patients with familial interstitial pneumonia (FIP) are at increased risk for pulmonary fibrosis. We assessed the prevalence and risk factors for preclinical pulmonary fibrosis (PrePF) in first-degree relatives of patients with FIP and determined the utility of deep learning in detecting PrePF on CT.MethodsFirst-degree relatives of patients with FIP over 40 years of age who believed themselves to be unaffected by pulmonary fibrosis underwent CT scans of the chest. Images were visually reviewed, and a deep learning algorithm was used to quantify lung fibrosis. Genotyping for common idiopathic pulmonary fibrosis risk variants in MUC5B and TERT was performed.FindingsIn 494 relatives of patients with FIP from 263 families of patients with FIP, the prevalence of PrePF on visual CT evaluation was 15.6% (95% CI 12.6 to 19.0). Compared with visual CT evaluation, deep learning quantitative CT analysis had 84% sensitivity (95% CI 0.72 to 0.89) and 86% sensitivity (95% CI 0.83 to 0.89) for discriminating subjects with visual PrePF diagnosis. Subjects with PrePF were older (65.9, SD 10.1 years) than subjects without fibrosis (55.8 SD 8.7 years), more likely to be male (49% vs 37%), more likely to have smoked (44% vs 27%) and more likely to have the MUC5B promoter variant rs35705950 (minor allele frequency 0.29 vs 0.21). MUC5B variant carriers had higher quantitative CT fibrosis scores (mean difference of 0.36%), a difference that remains significant when controlling for age and sex.InterpretationPrePF is common in relatives of patients with FIP. Its prevalence increases with age and the presence of a common MUC5B promoter variant. Quantitative CT analysis can detect these imaging abnormalities.

Quantitative CT analysis of honeycombing area in idiopathic pulmonary fibrosis: Correlations with pulmonary function tests

2016 ◽  
Vol 85 (1) ◽  
pp. 125-130 ◽  
Author(s):  
Hiroaki Nakagawa ◽  
Yukihiro Nagatani ◽  
Masashi Takahashi ◽  
Emiko Ogawa ◽  
Nguyen Van Tho ◽  
...  
Keyword(s):  
Idiopathic Pulmonary Fibrosis ◽  
Pulmonary Fibrosis ◽  
Pulmonary Function ◽  
Pulmonary Function Tests ◽  
Quantitative Ct ◽  
Function Tests ◽  
Ct Analysis ◽  
Quantitative Ct Analysis

Quantitative CT Analysis in Chronic Hypersensitivity Pneumonitis: A Convolutional Neural Network Approach

2020 ◽  
Author(s):  
Lorenzo Aliboni ◽  
Olívia Meira Dias ◽  
Francesca Pennati ◽  
Bruno Guedes Baldi ◽  
Marcio Valente Yamada Sawamura ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Hypersensitivity Pneumonitis ◽  
Network Approach ◽  
Quantitative Ct ◽  
Neural Network Approach ◽  
Ct Analysis ◽  
Chronic Hypersensitivity Pneumonitis ◽  
Quantitative Ct Analysis

Identification of Pathological UIP in Patients with an Alternative Diagnosis (to IPF) Pattern using Quantitative CT Analysis

2019 ◽  
Author(s):  
J. Chung ◽  
A.O. Adegunsoye ◽  
P. Thirkateh ◽  
S. Montner ◽  
C. Lee ◽  
...  
Keyword(s):  
Quantitative Ct ◽  
Alternative Diagnosis ◽  
Ct Analysis ◽  
Quantitative Ct Analysis
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