scholarly journals PECULIARITIES OF BRONCHIAL ASTHMA CLINICAL COURSE IN SMOKERS WITH SMALL AIRWAY DISEASES

2020 ◽  
pp. 8-21
Author(s):  
V.V. Gnoevykh ◽  
Yu.A. Shorokhova ◽  
A.Yu. Smirnova ◽  
A.B. Peskov ◽  
V.A. Razin
Keyword(s):  
Bronchial Asthma ◽  
Clinical Course ◽  
Airway Disease ◽  
Small Airway ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Small Airway Disease ◽  
Airway Diseases ◽  
Related Phenotype ◽  
Keywords Bronchial Asthma

The literature review provides up-to-date information about the clinical course of bronchial asthma (BA) in smokers with small airway diseases. Special attention is paid to the combination of bronchial asthma and chronic obstructive pulmonary disease (COPD), namely asthma-COPD overlap syndrome (ACOS). According to literature data, in case of small airway duseases exacerbations are more often and severe in smokers with BA and ACOS. Besides, disease prognosis worsens due to reduction in the efficacy of a baseline therapy. Keywords: bronchial asthma, small airway disease, smoking-related phenotype, asthma-COPD overlap (BA-COPD phenotype). В литературном обзоре представлены современные сведения об особенностях клинического течения бронхиальной астмы (БА) у курильщиков с поражением малых дыхательных путей (МДП). Особое внимание уделено сочетанию бронхиальной астмы и хронической обструктивной болезни лёгких (ХОБЛ; COPD) – синдрому перекрёста БА-ХОБЛ (СПБАХ, asthma-COPD overlap, ACO; фенотип БА-ХОБЛ). Согласно литературным данным, в случае поражения МДП у больных БА с фенотипом курильщика и при сочетании БА-ХОБЛ чаще возникают и тяжелее протекают обострения, ухудшается прогноз заболевания, в т.ч. из-за снижения эффективности базисной терапии. Ключевые слова: бронхиальная астма, поражение малых дыхательных путей, фенотип курильщика, asthma-COPD overlap (фенотип БА-ХОБЛ).

scholarly journals Computed tomography airway tree tortuosity features predict functional small airway disease in chronic obstructive pulmonary disease

2021 ◽  
Author(s):  
Daniel Genkin ◽  
Danesh Aslam ◽  
Jason Bartlett
Keyword(s):  
Computed Tomography ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Airway Disease ◽  
Small Airway ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Small Airway Disease ◽  
Full Inspiration ◽  
Airway Tree

Over 1 000 000 Canadians are diagnosed with Chronic Obstructive Pulmonary Disease (COPD) and by 2020 the disease will be the third deadliest on Earth. Despite high prevalence, diagnosis of COPD occurs late in the disease course, after a large portion of the small airways are destroyed. Current methods to quantify small airway disease (SAD) using the Disease Probability Measure (DPM) approach requires CT images acquired at full inspiration and full expiration, and therefore there are technical challenges and dose concerns Computed Tomography (CT) imaging using only a single full inspiration CT image can be used segment the central airway tree and generate quantitative morphometric measurements.

scholarly journals Computed tomography airway tree tortuosity features predict functional small airway disease in chronic obstructive pulmonary disease

2021 ◽  
Author(s):  
Daniel Genkin ◽  
Danesh Aslam ◽  
Jason Bartlett
Keyword(s):  
Computed Tomography ◽  
Chronic Obstructive Pulmonary Disease ◽  
Pulmonary Disease ◽  
Airway Disease ◽  
Small Airway ◽  
Chronic Obstructive ◽  
Obstructive Pulmonary Disease ◽  
Small Airway Disease ◽  
Full Inspiration ◽  
Airway Tree

Over 1 000 000 Canadians are diagnosed with Chronic Obstructive Pulmonary Disease (COPD) and by 2020 the disease will be the third deadliest on Earth. Despite high prevalence, diagnosis of COPD occurs late in the disease course, after a large portion of the small airways are destroyed. Current methods to quantify small airway disease (SAD) using the Disease Probability Measure (DPM) approach requires CT images acquired at full inspiration and full expiration, and therefore there are technical challenges and dose concerns Computed Tomography (CT) imaging using only a single full inspiration CT image can be used segment the central airway tree and generate quantitative morphometric measurements.

scholarly journals A 3D-CNN model with CT-based parametric response mapping for classifying COPD subjects

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Thao Thi Ho ◽  
Taewoo Kim ◽  
Woo Jin Kim ◽  
Chang Hyun Lee ◽  
Kum Ju Chae ◽  
...  
Keyword(s):  
Neural Network ◽  
Pulmonary Function Tests ◽  
Airway Disease ◽  
Chronic Obstructive ◽  
Respiratory Disorder ◽  
Obstructive Pulmonary Disease ◽  
Response Mapping ◽  
Small Airway Disease ◽  
3D Cnn ◽  
Parametric Response

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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