title: Quantitative computed tomographic imaging-based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes.
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.
authors.affiliation: 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.
authors.forename: Sanghun
authors.initials: S
authors.lastname: Choi
authors.affiliation: 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.
authors.forename: Eric A
authors.initials: EA
authors.lastname: Hoffman
authors.affiliation: Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pittsburgh, Pittsburgh, Pa.
authors.forename: Sally E
authors.initials: SE
authors.lastname: Wenzel
authors.affiliation: Departments of Internal Medicine and Pediatrics, Washington University School of Medicine, St Louis, Mo.
authors.forename: Mario
authors.initials: M
authors.lastname: Castro
authors.affiliation: School of Medicine & Public Health, University of Wisconsin, Madison, Wis.
authors.forename: Sean
authors.initials: S
authors.lastname: Fain
authors.affiliation: School of Medicine & Public Health, University of Wisconsin, Madison, Wis.
authors.forename: Nizar
authors.initials: N
authors.lastname: Jarjour
authors.affiliation: School of Medicine & Public Health, University of Wisconsin, Madison, Wis.
authors.forename: Mark L
authors.initials: ML
authors.lastname: Schiebler
authors.affiliation: Department of Statistics, University of Connecticut, Storrs, Conn.
authors.forename: Kun
authors.initials: K
authors.lastname: Chen
authors.affiliation: 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.
authors.forename: Ching-Long
authors.initials: CL
authors.lastname: Lin
authors.affiliation: Severe Asthma Research Program, Bethesda, Md.
mesh_terms: D000328:Adult; D001249:Asthma / Q000145:classification* / Q000000981:diagnostic imaging* / Q000473:pathology; D016000:Cluster Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010641:Phenotype; D025341:Principal Component Analysis; D012720:Severity of Illness Index; D014057:Tomography, X-Ray Computed; D055815:Young Adult
keywords: Computed tomography; air trapping; airway circularity; cluster analysis; image processing; image registration; luminal narrowing; neutrophilic asthma; severe asthma; wall thickening