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

Research output: Contribution to journalArticle

33 Scopus citations

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.

Original languageEnglish
Pages (from-to)690-700.e8
JournalJournal of Allergy and Clinical Immunology
Volume140
Issue number3
DOIs
StatePublished - Sep 2017

Keywords

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

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    Choi, S., Hoffman, E. A., Wenzel, S. E., Castro, M., Fain, S., Jarjour, N., Schiebler, M. L., Chen, K., & Lin, C. L. (2017). Quantitative computed tomographic imaging–based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes. Journal of Allergy and Clinical Immunology, 140(3), 690-700.e8. https://doi.org/10.1016/j.jaci.2016.11.053