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

Sanghun Choi1, Eric A Hoffman2, Sally E Wenzel3

  • 1Department 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.

The Journal of Allergy and Clinical Immunology
|February 2, 2017
PubMed
Summary

This study identified four distinct asthma subgroups using imaging data. These clusters correlate with clinical features, aiding in the development of targeted therapies for severe asthma.

Keywords:
Computed tomographyair trappingairway circularitycluster analysisimage processingimage registrationluminal narrowingneutrophilic asthmasevere asthmawall thickening

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Area of Science:

  • Pulmonary Medicine
  • Radiology
  • Computational Biology

Background:

  • Imaging metrics like airway diameter, wall thickness, and air trapping are crucial for distinguishing severe asthma from nonsevere asthma and healthy individuals.
  • Quantitative computed tomography (CT) provides valuable structural and functional data for asthma assessment.

Purpose of the Study:

  • To identify distinct asthma patient clusters based on quantitative CT imaging features.
  • To investigate the association between these imaging-derived clusters and established clinical metrics.

Main Methods:

  • A cluster analysis was performed on quantitative CT data from 248 asthma patients, analyzing inspiration and expiration scans.
  • Multiscale imaging variables, including airway dimensions, air trapping, and lung deformation, were utilized.
  • Derived asthma subgroups were correlated with demographics, questionnaires, medication history, and biomarkers.

Main Results:

  • Four distinct imaging-based clusters emerged.
  • Cluster 1: Young, nonsevere asthma with reversible obstruction. Cluster 2: Mixed severity with airway narrowing. Clusters 3 & 4: Predominantly severe asthma, characterized by obesity, airway wall thickening, late-onset, persistent obstruction, air trapping, and reduced deformation.
  • Clusters 3 and 4 showed distinct immune profiles (lymphocyte/neutrophil counts).

Conclusions:

  • Four distinct asthma subgroups were identified using imaging data.
  • These clusters show significant correlations with clinical characteristics.
  • This imaging-based stratification can inform the development of novel, targeted therapies for specific asthma subgroups.