Related Experiment Video
Updated: Sep 25, 2025

Methodology for Sputum Induction and Laboratory Processing
Published on: December 17, 2017
Quantitative CT Characteristics of Cluster Phenotypes in the Severe Asthma Research Program Cohorts
Abhaya P Trivedi1, Chase Hall1, Charles W Goss1
1From the Division of Pulmonary, Critical Care, and Sleep Medicine, Rush University Medical Center, Chicago, Ill (A.P.T.); Division of Pulmonary and Critical Care Medicine, University of Kansas School of Medicine, Kansas City, KS 66103-2937 (C.H., M.C.); Division of Biostatistics (C.W.G., D.L., K.B.S.), Division of Pulmonary and Critical Care Medicine (J.G.K., M.C.M., M.S.), Washington University School of Medicine, St Louis, Mo; Department of Radiology, University of Iowa, Iowa City, Iowa (J.P.S., J.M.R., J.D.N., E.A.H.); Department of Medicine, University of Arizona, Tucson, Ariz (H.L., E.R.B., D.A.M.); VIDA Diagnostics, Coralville, Iowa (J.S., S.M., S.P.); Section of Pulmonary, Critical Care, Allergy and Immunologic Diseases, Wake Forest University School of Medicine, Winston-Salem, NC (W.C.M.); Division of Pulmonary and Critical Care Medicine, Brigham and Women's Hospital, Boston, Mass (E.I., G.R.W., B.D.L.); Department of Radiology (J.K.L.) and Division of Pulmonary, Allergy and Critical Care Medicine (S.E.W.), University of Pittsburgh, Pittsburgh, Pa; Division of Pulmonary and Critical Care Medicine, University of California, San Francisco, San Francisco, Calif (J.V.F.); Department of Radiology (M.L.S., S.B.F.) and Division of Allergy, Pulmonary and Critical Care Medicine (N.N.J.), University of Wisconsin, Madison, Wis; Department of Public Health Sciences, Penn State Eberly College of Science, University Park, Pa (D.T.M.); and Department of Pulmonary Medicine, The University of Texas MD Anderson Cancer Center, Houston, Tex (A.S.).
None:
Background Clustering key clinical characteristics of participants in the Severe Asthma Research Program (SARP), a large, multicenter prospective observational study of patients with asthma and healthy controls, has led to the identification of novel asthma phenotypes. Purpose To determine whether quantitative CT (qCT) could help distinguish between clinical asthma phenotypes. Materials and Methods A retrospective cross-sectional analysis was conducted with the use of qCT images (maximal bronchodilation at total lung capacity [TLC], or inspiration, and functional residual capacity [FRC], or expiration) from the cluster phenotypes of SARP participants (cluster 1: minimal disease; cluster 2: mild, reversible; cluster 3: obese asthma; cluster 4: severe, reversible; cluster 5: severe, irreversible) enrolled between September 2001 and December 2015. Airway morphometry was performed along standard paths (RB1, RB4, RB10, LB1, and LB10). Corresponding voxels from TLC and FRC images were mapped with use of deformable image registration to characterize disease probability maps (DPMs) of functional small airway disease (fSAD), voxel-level volume changes (Jacobian), and isotropy (anisotropic deformation index [ADI]). The association between cluster assignment and qCT measures was evaluated using linear mixed models. Results A total of 455 participants were evaluated with cluster assignments and CT (mean age ± SD, 42.1 years ± 14.7; 270 women). Airway morphometry had limited ability to help discern between clusters. DPM fSAD was highest in cluster 5 (cluster 1 in SARP III: 19.0% ± 20.6; cluster 2: 18.9% ± 13.3; cluster 3: 24.9% ± 13.1; cluster 4: 24.1% ± 8.4; cluster 5: 38.8% ± 14.4; P < .001). Lower whole-lung Jacobian and ADI values were associated with greater cluster severity. Compared to cluster 1, cluster 5 lung expansion was 31% smaller (Jacobian in SARP III cohort: 2.31 ± 0.6 vs 1.61 ± 0.3, respectively, P < .001) and 34% more isotropic (ADI in SARP III cohort: 0.40 ± 0.1 vs 0.61 ± 0.2, P < .001). Within-lung Jacobian and ADI SDs decreased as severity worsened (Jacobian SD in SARP III cohort: 0.90 ± 0.4 for cluster 1; 0.79 ± 0.3 for cluster 2; 0.62 ± 0.2 for cluster 3; 0.63 ± 0.2 for cluster 4; and 0.41 ± 0.2 for cluster 5; P < .001). Conclusion Quantitative CT assessments of the degree and intraindividual regional variability of lung expansion distinguished between well-established clinical phenotypes among participants with asthma from the Severe Asthma Research Program study. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Verschakelen in this issue.
More Related Videos
02:09Multi-modal Pulmonary Imaging: Using Complementary Information from CT and Hyperpolarized 129Xe MRI to Evaluate Lung Structure-Function
Published on: April 12, 2024
08:17Generation of a Chronic Obstructive Pulmonary Disease Model in Mice by Repeated Ozone Exposure
Published on: August 25, 2017
Related Concept Videos
Asthma-II: Pathophysiology and Classification
Additionally, environmental and genetic factors play crucial roles in determining an individual's susceptibility to asthma and the severity of their condition.
Critical processes in asthma pathophysiology include:
Asthma-IV: Diagnostic and Management
Clinical Assessment for Asthma:
This is the first step in diagnosing and managing asthma. It includes:
Asthma-III: Symptoms and Complications
Classification of Asthma
Chronic Obstructive Pulmonary Disease-IV: Assessement and Diagnostic Studies
Medical History
Asthma-I: Introduction
Asthma: Pathogenesis and Management
Asthma is classified as allergic and non-allergic. Allergens such as dust mites, pollen, and pet dander trigger allergic asthma, while factors like cold air, intense emotions, or exercise can induce non-allergic asthma.