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J Allergy Clin Immunol. 2017 Sep;140(3):690-700.e8. doi: 10.1016/j.jaci.2016.11.053. Epub 2017 Jan 29.

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

Author information

1
Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa; IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa; Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa.
2
Department of Biomedical Engineering, University of Iowa, Iowa City, Iowa; Department of Radiology, University of Iowa, Iowa City, Iowa; Department of Internal Medicine, University of Iowa, Iowa City, Iowa.
3
Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pa.
4
Departments of Internal Medicine and Pediatrics, Washington University School of Medicine, St Louis, Mo.
5
School of Medicine & Public Health, University of Wisconsin, Madison, Wis.
6
Department of Statistics, University of Connecticut, Storrs, Conn.
7
Department of Mechanical and Industrial Engineering, University of Iowa, Iowa City, Iowa; IIHR-Hydroscience and Engineering, University of Iowa, Iowa City, Iowa; Department of Radiology, University of Iowa, Iowa City, Iowa. Electronic address: ching-long-lin@uiowa.edu.
8
Severe Asthma Research Program, Bethesda, Md.

Abstract

BACKGROUND:

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.

OBJECTIVE:

The objective of this study was to identify imaging-based clusters and to explore the association of the clusters with existing clinical metrics.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

Computed tomography; air trapping; airway circularity; cluster analysis; image processing; image registration; luminal narrowing; neutrophilic asthma; severe asthma; wall thickening

PMID:
28143694
PMCID:
PMC5534190
DOI:
10.1016/j.jaci.2016.11.053
[Indexed for MEDLINE]
Free PMC Article

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