Quantitative CT analysis of pulmonary nodules for lung adenocarcinoma risk classification based on an exponential weighted grey scale angular density distribution feature

2018 ◽  
Vol 160 ◽  
pp. 141-151 ◽  
Author(s):  
Vanbang Le ◽  
Dawei Yang ◽  
Yu Zhu ◽  
Bingbing Zheng ◽  
Chunxue Bai ◽  
...  
Keyword(s):  
Lung Adenocarcinoma ◽  
Density Distribution ◽  
Pulmonary Nodules ◽  
Risk Classification ◽  
Quantitative Ct ◽  
Angular Density ◽  
Grey Scale ◽  
Ct Analysis ◽  
Quantitative Ct Analysis

A standard phantom for quantitative CT analysis of pulmonary nodules.

Radiology ◽  
1983 ◽  
Vol 149 (3) ◽  
pp. 767-773 ◽  
Author(s):  
E A Zerhouni ◽  
M Boukadoum ◽  
M A Siddiky ◽  
J M Newbold ◽  
D C Stone ◽  
...  
Keyword(s):  
Pulmonary Nodules ◽  
Quantitative Ct ◽  
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.

Sign in / Sign up

Export Citation Format

Share Document