scholarly journals Bullous Parametric Response Map for Functional Localization of COPD

2022 ◽  
Author(s):  
Kuo-Lung Lor ◽  
Yeun-Chung Chang ◽  
Chong-Jen Yu ◽  
Cheng-Yi Wang ◽  
Chung-Ming Chen ◽  
...  
Keyword(s):  
Target Selection ◽  
Airway Disease ◽  
Routine Care ◽  
Small Airway ◽  
Airflow Limitation ◽  
Normal Lung ◽  
Severe Emphysema ◽  
Small Airway Disease ◽  
Functional Localization ◽  
Parametric Response

AbstractAdvanced bronchoscopic lung volume reduction treatment (BLVR) is now a routine care option for treating patients with severe emphysema. Patterns of low attenuation clusters indicating emphysema and functional small airway disease (fSAD) on paired CT, which may provide additional insights to the target selection of the segmental or subsegmental lobe of the treatments, require further investigation. The low attenuation clusters (LACS) were segmented to identify the scalar and spatial distribution of the lung destructions, in terms of 10 fractions scales of low attenuation density (LAD) located in upper lobes and lower lobes. The LACs of functional small airway disease (fSAD) were delineated by applying the technique of parametric response map (PRM) on the co-registered CT image data. Both emphysematous LACs of inspiratory CT and fSAD LACs on expiratory CT were used to derive the coefficients of the predictive model for estimating the airflow limitation. The voxel-wise severity is then predicted using the regional LACs on the co-registered CT to indicate the functional localization, namely, the bullous parametric response map (BPRM). A total of 100 subjects, 88 patients with mild to very severe COPD and 12 control participants with normal lung functions (FEV1/FVC % > 70%), were evaluated. Pearson’s correlations between FEV1/FVC% and LAV%HU-950 of severe emphysema are − 0.55 comparing to − 0.67 and − 0.62 of LAV%HU-856 of air-trapping and LAV%fSAD respectively. Pearson’s correlation between FEV1/FVC% and FEV1/FVC% predicted by the proposed model using LAD% of HU-950 and fSAD on BPRM is 0.82 (p < 0.01). The result of the Bullous Parametric Response Map (BPRM) is capable of identifying the less functional area of the lung, where the BLVR treatment is aimed at removing from a hyperinflated area of emphysematous regions.

scholarly journals The Diagnostic Value of Forced Expiratory Time

2011 ◽  
Vol 1 (1) ◽  
pp. 39-42
Author(s):  
Siraj O. Wali
Keyword(s):  
Tertiary Care ◽  
Airway Disease ◽  
Diagnostic Value ◽  
Forced Expiratory Volume ◽  
Small Airway ◽  
Airflow Limitation ◽  
Forced Expiration ◽  
Care Hospital ◽  
Expiratory Time ◽  
Small Airway Disease

Objective: Airway obstruction can be clinically quantified at the bedside by measuring the time taken for forced expiration. The aim of this study was to examine the accuracy of the forced expiratory time in detecting airflow limitation, and small airway disease when compared with simple spirometry as a gold standard test. Method: Simple spirometry and forced expiratory time were performed on 201 subjects (age range; 12-81 years), referred to a pulmonary function laboratory at a tertiary care hospital. The diagnostic accuracy of forced expiratory time and its correlation with spirometric parameters were tested. Forced expiratory time > 6 seconds was regarded as abnormal, and the ratio of forced expiratory volume in the first second to forced vital capacity of < 70% was considered indicative of an airflow limitation. Results: Forced expiratory time was found to correlate weakly with spirometric parameters. Forced expiratory time at a cut-off value of => 6 seconds had a sensitivity of 61% and a specificity of 79% in predicting obstructive airway disease when compared with simple spirometry. On the other hand, the sensitivity and the specificity of forced expiratory time in predicting small airway disease were 47% and 86%, respectively. Conclusion: Forced expiratory time does not correlate well with all parameters of a simple spirometry. Its sensitivity and specificity for detecting airflow limitation and small airway disease were not high enough to be used as a diagnostic test. However, it may be effective enough to be utilized to confirm the diagnosis of small airway disease.

scholarly journals The Diagnostic Value of Forced Expiratory Time

2011 ◽  
Vol 1 (1) ◽  
pp. 39-42
Author(s):  
Siraj O. Wali
Keyword(s):  
Tertiary Care ◽  
Airway Disease ◽  
Diagnostic Value ◽  
Forced Expiratory Volume ◽  
Small Airway ◽  
Airflow Limitation ◽  
Forced Expiration ◽  
Care Hospital ◽  
Expiratory Time ◽  
Small Airway Disease

Objective: Airway obstruction can be clinically quantified at the bedside by measuring the time taken for forced expiration. The aim of this study was to examine the accuracy of the forced expiratory time in detecting airflow limitation, and small airway disease when compared with simple spirometry as a gold standard test. Method: Simple spirometry and forced expiratory time were performed on 201 subjects (age range; 12-81 years), referred to a pulmonary function laboratory at a tertiary care hospital. The diagnostic accuracy of forced expiratory time and its correlation with spirometric parameters were tested. Forced expiratory time > 6 seconds was regarded as abnormal, and the ratio of forced expiratory volume in the first second to forced vital capacity of < 70% was considered indicative of an airflow limitation. Results: Forced expiratory time was found to correlate weakly with spirometric parameters. Forced expiratory time at a cut-off value of => 6 seconds had a sensitivity of 61% and a specificity of 79% in predicting obstructive airway disease when compared with simple spirometry. On the other hand, the sensitivity and the specificity of forced expiratory time in predicting small airway disease were 47% and 86%, respectively. Conclusion: Forced expiratory time does not correlate well with all parameters of a simple spirometry. Its sensitivity and specificity for detecting airflow limitation and small airway disease were not high enough to be used as a diagnostic test. However, it may be effective enough to be utilized to confirm the diagnosis of small airway disease.

scholarly journals Gene expression profiling of bronchial brushes is associated with the level of emphysema measured by computed tomography-based parametric response mapping

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 ◽  
...  
Keyword(s):  
Gene Expression ◽  
Computed Tomography ◽  
Airway Disease ◽  
Small Airway ◽  
Study Cohort ◽  
Small Airways ◽  
Air Trapping ◽  
Response Mapping ◽  
Small Airway Disease ◽  
Parametric Response

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.

scholarly journals Central airway and peripheral lung structures in airway disease dominant COPD

2021 ◽  
pp. 00672-2020
Author(s):  
Naoya Tanabe ◽  
Kaoruko Shimizu ◽  
Kunihiko Terada ◽  
Susumu Sato ◽  
Masaru Suzuki ◽  
...  
Keyword(s):  
Airway Disease ◽  
Small Airway ◽  
Airflow Limitation ◽  
Volume Percent ◽  
Wall Area ◽  
Small Airway Disease ◽  
Peripheral Lung ◽  
Airway Tree ◽  
N 93 ◽  
Central Airway

The concept that the small airway is a primary pathological site for all COPD phenotypes has been challenged by recent findings that the disease starts from the central airways in COPD subgroups and that a smaller central airway tree increases COPD risk. This study aimed to examine whether the computed tomography (CT)-based airway disease-dominant (AD) subtype, defined using the central airway dimension, was less associated with small airway dysfunction (SAD) on CT, compared to the emphysema-dominant (ED) subtype.COPD patients were categorised into mild, AD, ED, and mixed groups based on wall area percent (WA%) of the segmental airways and low attenuation volume percent in the Kyoto-Himeji (n=189) and Hokkaido COPD cohorts (n=93). The volume percent of SAD regions (SAD%) was obtained by nonrigidly registering inspiratory and expiratory CT.The AD group had a lower SAD% than the ED group and similar SAD% to the mild group. The AD group had a smaller lumen size of airways proximal to the segmental airways and more frequent asthma history before age 40 years than the ED group. In multivariable analyses, while the AD and ED groups were similarly associated with greater airflow limitation, the ED, but not the AD group, was associated with greater SAD%, whereas the AD, but not the ED group, was associated with a smaller central airway size.The CT-based AD COPD subtype might be associated with a smaller central airway tree and asthma history, but not with peripheral lung pathologies including small airway disease, unlike the ED subtype.

Small Airway Disease after Mycoplasma Pneumonia in Children: HRCT Findings and Correlation with Radiographic Findings

2003 ◽  
Vol 48 (4) ◽  
pp. 361
Author(s):  
Jung Eun Cheon ◽  
Woo Sun Kim ◽  
In One Kim ◽  
Young Yull Koh ◽  
Hoan Jong Lee ◽  
...  
Keyword(s):  
Airway Disease ◽  
Small Airway ◽  
Radiographic Findings ◽  
Small Airway Disease ◽  
Mycoplasma Pneumonia

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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