Quantitative computed tomographic imaging–based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes

Sanghun Choi; Eric A Hoffman; Sally E Wenzel; Mario Castro; Sean Fain; Nizar Jarjour; Mark L Schiebler; Kun Chen; Ching-Long Lin

doi:10.1016/j.jaci.2016.11.053

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Quantitative computed tomographic imaging–based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes

Journal article

Open access

Peer reviewed

Quantitative computed tomographic imaging–based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes

Sanghun Choi, Eric A Hoffman, Sally E Wenzel, Mario Castro, Sean Fain, Nizar Jarjour, Mark L Schiebler, Kun Chen and Ching-Long Lin

Journal of allergy and clinical immunology, Vol.140(3), pp.690-700.e8

09/2017

DOI: 10.1016/j.jaci.2016.11.053

PMCID: PMC5534190

PMID: 28143694

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Abstract

Imaging variables, including airway diameter, wall thickness, and air trapping, have been found to be important metrics when differentiating patients with severe asthma from those with nonsevere asthma and healthy subjects. The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics. We performed an imaging-based cluster analysis using quantitative computed tomography–based structural and functional variables extracted from the respective inspiration and expiration scans of 248 asthmatic patients. The imaging-based metrics included a broader set of multiscale variables, such as inspiratory airway dimension, expiratory air trapping, and registration-based lung deformation (inspiration vs expiration). Asthma subgroups derived from a clustering method were associated with subject demographics, questionnaire results, medication history, and biomarker variables. Cluster 1 was composed of younger patients with early-onset nonsevere asthma and reversible airflow obstruction and normal airway structure. Cluster 2 was composed of patients with a mix of patients with nonsevere and severe asthma with marginal inflammation who exhibited airway luminal narrowing without wall thickening. Clusters 3 and 4 were dominated by patients with severe asthma. Cluster 3 patients were obese female patients with reversible airflow obstruction who exhibited airway wall thickening without airway narrowing. Cluster 4 patients were late-onset older male subjects with persistent airflow obstruction who exhibited significant air trapping and reduced regional deformation. Cluster 3 and 4 patients also showed decreased lymphocyte and increased neutrophil counts, respectively. Four image-based clusters were identified and shown to be correlated with clinical characteristics. Such clustering serves to differentiate asthma subgroups that can be used as a basis for the development of new therapies. [Display omitted]

image processing

luminal narrowing

Computed tomography

image registration

airway circularity

neutrophilic asthma

air trapping

severe asthma

wall thickening

cluster analysis

Details

Title: Subtitle: Quantitative computed tomographic imaging–based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes
Creators: Sanghun Choi - Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa
Eric A Hoffman - Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa
Sally E Wenzel - Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pa
Mario Castro - Departments of Internal Medicine and Pediatrics, Washington University School of Medicine, St Louis, Mo
Sean Fain - School of Medicine & Public Health, University of Wisconsin, Madison, Wis
Nizar Jarjour - School of Medicine & Public Health, University of Wisconsin, Madison, Wis
Mark L Schiebler - School of Medicine & Public Health, University of Wisconsin, Madison, Wis
Kun Chen - Department of Statistics, University of Connecticut, Storrs, Conn
Ching-Long Lin - Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa
Resource Type: Journal article
Publication Details: Journal of allergy and clinical immunology, Vol.140(3), pp.690-700.e8
Publisher: Elsevier Inc
DOI: 10.1016/j.jaci.2016.11.053
PMID: 28143694
PMCID: PMC5534190
ISSN: 0091-6749
eISSN: 1097-6825
Grant note: U01 HL114494; HL109152; R01 HL094315; HL112986; HL69174; HL064368; HL091762; HL069116; S10 RR022421; U10 HL109257; HL109168; UL1 RR024153; UL1 TR000448; UL1 TR000427 / National Institutes of Health grants
Language: English
Date published: 09/2017
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering; Iowa Neuroscience Institute; Health and Human Physiology; Mechanical Engineering; Internal Medicine
Record Identifier: 9984051515302771

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