Developed over six decades ago, pulmonary oscillometry has re‐emerged as a noninvasive and effort‐independent method for evaluating respiratory‐system impedance in patients with obstructive lung disease. Here, we evaluated the relationships between hyperpolarized 3He ventilation‐defect‐percent (VDP) and respiratory‐system resistance, reactance and reactance area (AX) measurements in 175 participants including 42 never‐smokers without respiratory disease, 56 ex‐smokers with chronic‐obstructive‐pulmonary‐disease (COPD), 28 ex‐smokers without COPD and 49 asthmatic never‐smokers. COPD participants were dichotomized based on x‐ray computed‐tomography (CT) evidence of emphysema (relative‐area CT‐density‐histogram ≤ 950HU (RA 950) ≥ 6.8%). In asthma and COPD subgroups, MRI VDP was significantly related to the frequency‐dependence of resistance (R 5‐19; asthma: ρ = 0.48, P = 0.0005; COPD: ρ = 0.45, P = 0.0004), reactance at 5 Hz (X 5: asthma, ρ = −0.41, P = 0.004; COPD: ρ = −0.38, P = 0.004) and AX (asthma: ρ = 0.47, P = 0.0007; COPD: ρ = 0.43, P = 0.0009). MRI VDP was also significantly related to R 5‐19 in COPD participants without emphysema (ρ = 0.54, P = 0.008), and to X 5 in COPD participants with emphysema (ρ = −0.36, P = 0.04). AX was weakly related to VDP in asthma (ρ = 0.47, P = 0.0007) and COPD participants with (ρ = 0.39, P = 0.02) and without (ρ = 0.43, P = 0.04) emphysema. AX is sensitive to obstruction but not specific to the type of obstruction, whereas the different relationships for MRI VDP with R 5‐19 and X 5 may reflect the different airway and parenchymal disease‐specific biomechanical abnormalities that lead to ventilation defects.
Background: Fixed airflow limitation and ventilation heterogeneity are common in chronic obstructive pulmonary disease (COPD). Conventional noncontrast CT provides airway and parenchymal measurements but cannot be used to directly determine lung function. Purpose: To develop, train, and test a CT texture analysis and machine-learning algorithm to predict lung ventilation heterogeneity in participants with COPD. Materials and Methods: In this prospective study (ClinicalTrials.gov: NCT02723474; conducted from January 2010 to February 2017), participants were randomized to optimization (n = 1), training (n = 67), and testing (n = 27) data sets. Hyperpolarized (HP) helium 3 (3 He) MRI ventilation maps were co-registered with thoracic CT to provide ground truth labels, and 87 quantitative imaging features were extracted and normalized to lung averages to generate 174 features. The volume-of-interest dimension and the training data sampling method were optimized to maximize the area under the receiver operating characteristic curve (AUC). Forward feature selection was performed to reduce the number of features; logistic regression, linear support vector machine, and quadratic support vector machine classifiers were trained through fivefold cross validation. The highest-performing classification model was applied to the test data set. Pearson coefficients were used to determine the relationships between the model, MRI, and pulmonary function measurements. Results: The quadratic support vector machine performed best in training and was applied to the test data set. Model-predicted ventilation maps had an accuracy of 88% (95% confidence interval [CI]: 88%, 88%) and an AUC of 0.82 (95% CI: 0.82, 0.83) when the HP 3 He MRI ventilation maps were used as the reference standard. Model-predicted ventilation defect percentage (VDP) was correlated with VDP at HP 3 He MRI (r = 0.90, P , .001). Both model-predicted and HP 3 He MRI VDP were correlated with forced expiratory volume in 1 second (FEV 1
I n patients with chronic obstructive pulmonary disease (COPD), irreversible airflow obstruction related to parenchymal destruction, airway remodeling, and luminal obstruction (1) drives debilitating symptoms, poor quality of life, and premature death. While forced expiratory volume in 1 second (FEV 1) remains a key clinical and diagnostic measurement of COPD, it cannot provide regional information, nor can it be used to adequately discriminate between patients with different underlying diseases, such as airspace enlargement and small airways disease, that manifest differently in individual patients. To address some of the limitations of spirometry measurements, thoracic x-ray CT has yielded quantitative airway (2) and parenchyma (3) information, as well as indirect information related to small airways disease generated using parametric response maps (4-6). Pulmonary CT has been used widely in studies such as the Genetic Epidemiology of Chronic Obstructive Pulmonary Disease, or COPDGene (7), Subpopulations and Intermediate Outcome Measures in COPD, or SPIROMICS (8), Multi-Ethnic Study of Atherosclerosis, or MESA (9), Canadian Cohort Obstructive Lung Disease, or CANCold (10), and Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints, or ECLIPSE (11). CT parametric response maps are used to quantify COPD severity (4,12), monitor
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.