scholarly journals Assessment of "Percent Functional Small Airways Disease" by Parametric Response Mapping as a Potential Radiographic Biomarker of Deployment-Related Constrictive Bronchiolitis

Author(s):  
C. Davis ◽  
C. Lopez ◽  
S. Murray ◽  
R.F. Miller ◽  
M.J. Falvo ◽  
...  
2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Rachel N. Criner ◽  
Charles R. Hatt ◽  
Craig J. Galbán ◽  
Ella A. Kazerooni ◽  
David A. Lynch ◽  
...  

Abstract Impaired single breath carbon monoxide diffusing capacity (DLCO) is associated with emphysema. Small airways disease (SAD) may be a precursor lesion to emphysema, but the relationship between SAD and DLCO is undescribed. We hypothesized that in mild COPD, functional SAD (fSAD) defined by computed tomography (CT) and Parametric Response Mapping methodology would correlate with impaired DLCO. Using data from ever-smokers in the COPDGene cohort, we established that fSAD correlated significantly with lower DLCO among both non-obstructed and GOLD 1–2 subjects. The relationship between DLCO with CT-defined emphysema was present in all GOLD stages, but most prominent in severe disease. Trial registration NCT00608764. Registry: COPDGene. Registered 06 February 2008, retrospectively registered.


2020 ◽  
Vol 318 (6) ◽  
pp. L1222-L1228
Author(s):  
Senani N. H. Rathnayake ◽  
Firdaus A. A. Mohamed Hoesein ◽  
Craig J. Galban ◽  
Nick H. T. ten Hacken ◽  
Brian G. G. Oliver ◽  
...  

Parametric response mapping (PRM) is a computed tomography (CT)-based method to phenotype patients with chronic obstructive pulmonary disease (COPD). It is capable of differentiating emphysema-related air trapping with nonemphysematous air trapping (small airway disease), which helps to identify the extent and localization of the disease. Most studies evaluating the gene expression in smokers and COPD patients related this to spirometric measurements, but none have investigated the relationship with CT-based measurements of lung structure. The current study aimed to examine gene expression profiles of brushed bronchial epithelial cells in association with the PRM-defined CT-based measurements of emphysema (PRMEmph) and small airway disease (PRMfSAD). Using the Top Institute Pharma (TIP) study cohort (COPD = 12 and asymptomatic smokers = 32), we identified a gene expression signature of bronchial brushings, which was associated with PRMEmph in the lungs. One hundred thirty-three genes were identified to be associated with PRMEmph. Among the most significantly associated genes, CXCL11 is a potent chemokine involved with CD8+ T cell activation during inflammation in COPD, indicating that it may play an essential role in the development of emphysema. The PRMEmph signature was then replicated in two independent data sets. Pathway analysis showed that the PRMEmph signature is associated with proinflammatory and notch signaling pathways. Together these findings indicate that airway epithelium may play a role in the development of emphysema and/or may act as a biomarker for the presence of emphysema. In contrast, its role in relation to functional small airways disease is less clear.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thao Thi Ho ◽  
Taewoo Kim ◽  
Woo Jin Kim ◽  
Chang Hyun Lee ◽  
Kum Ju Chae ◽  
...  

AbstractChronic obstructive pulmonary disease (COPD) is a respiratory disorder involving abnormalities of lung parenchymal morphology with different severities. COPD is assessed by pulmonary-function tests and computed tomography-based approaches. We introduce a new classification method for COPD grouping based on deep learning and a parametric-response mapping (PRM) method. We extracted parenchymal functional variables of functional small airway disease percentage (fSAD%) and emphysema percentage (Emph%) with an image registration technique, being provided as input parameters of 3D convolutional neural network (CNN). The integrated 3D-CNN and PRM (3D-cPRM) achieved a classification accuracy of 89.3% and a sensitivity of 88.3% in five-fold cross-validation. The prediction accuracy of the proposed 3D-cPRM exceeded those of the 2D model and traditional 3D CNNs with the same neural network, and was comparable to that of 2D pretrained PRM models. We then applied a gradient-weighted class activation mapping (Grad-CAM) that highlights the key features in the CNN learning process. Most of the class-discriminative regions appeared in the upper and middle lobes of the lung, consistent with the regions of elevated fSAD% and Emph% in COPD subjects. The 3D-cPRM successfully represented the parenchymal abnormalities in COPD and matched the CT-based diagnosis of COPD.


PET Clinics ◽  
2013 ◽  
Vol 8 (2) ◽  
pp. 201-217 ◽  
Author(s):  
Benjamin M. Ellingson ◽  
Wei Chen ◽  
Robert J. Harris ◽  
Whitney B. Pope ◽  
Albert Lai ◽  
...  

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