Emphysema

Chest Imaging ◽  
2019 ◽  
pp. 325-329
Author(s):  
Travis S. Henry ◽  
Brent P. Little

Emphysema is the abnormal, permanent enlargement of air spaces distal to the terminal bronchioles, accompanied by destruction of alveolar walls, but without obvious fibrosis. Chronic obstructive pulmonary disease (COPD) is a spectrum of obstructive lung diseases that includes emphysema and chronic bronchitis – diseases that frequently coexist, especially in smokers. Emphysema is an extremely common disease and in most cases the diagnosis is established with clinical data including pulmonary function tests (PFTs). CT may be helpful for clarifying the diagnosis in mild cases or if another disease process (such as interstitial lung disease) is suspected. The three different types of emphysema (centrilobular, paraseptal, and panlobular) affect different parts of the secondary pulmonary lobule and are easily distinguished on CT. Emphysema distorts the normal lung anatomy and can cause superimposed processes (e.g. pneumonia or pulmonary edema) to look atypical on chest radiography and CT. Similarly, lung cancer may have an unusual morphology when it arises within emphysematous lung. Cystic lung disease and honeycombing in pulmonary fibrosis should not be confused with emphysema. Cysts and honeycombing have defined walls on CT, whereas centrilobular emphysema manifests as areas of low attenuation without perceptible walls.

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Julie Ng ◽  
Gustavo Pacheco-Rodriguez ◽  
Lesa Begley ◽  
Yvonne J. Huang ◽  
Sergio Poli ◽  
...  

AbstractLymphangioleiomyomatosis (LAM) is a progressive cystic lung disease with mortality driven primarily by respiratory failure. Patients with LAM frequently have respiratory infections, suggestive of a dysregulated microbiome. Here we demonstrate that end-stage LAM patients have a distinct microbiome signature compared to patients with end-stage chronic obstructive pulmonary disease.


Author(s):  
M. L. Shteiner ◽  
A. V. Zhestkov ◽  
N. E. Lavrentieva

Chronic obstructive pulmonary disease is a common disease that could lead to death. Pathogenesis of COPD involves both genetic and environmental factors. Such unfavorable production factors as dust, smoke, toxic and biologically active substances are the causes of disease in 15% of cases.


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.


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