Quantitative computed tomographic imaging–based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes Sanghun Choi, PhD,

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Quantitative computed tomographic imaging–based clustering differentiates asthmatic subgroups with distinctive clinical phenotypes Sanghun Choi, PhD, Eric A. Hoffman, PhD, Sally E. Wenzel, MD, Mario Castro, MD, Sean Fain, PhD, Nizar Jarjour, MD, Mark L. Schiebler, MD, Kun Chen, PhD, Ching-Long Lin, PhD Journal of Allergy and Clinical Immunology Volume 140, Issue 3, Pages 690-700.e8 (September 2017) DOI: 10.1016/j.jaci.2016.11.053 Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Journal of Allergy and Clinical Immunology 2017 140, 690-700 Journal of Allergy and Clinical Immunology 2017 140, 690-700.e8DOI: (10.1016/j.jaci.2016.11.053) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 1 Multiscale imaging-based variables used for cluster analysis including A, airway structure at TLC; B, AirT% at FRC; C, lung shape at TLC; D, registration-based function of TLC vs FRC. Labels of 10 selected local regions are shown in A. Journal of Allergy and Clinical Immunology 2017 140, 690-700.e8DOI: (10.1016/j.jaci.2016.11.053) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 2 Summary of respective imaging clusters. Note that most of the patients with severe asthma were in clusters 3 and 4. Journal of Allergy and Clinical Immunology 2017 140, 690-700.e8DOI: (10.1016/j.jaci.2016.11.053) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig 3 A simple scheme for predicting image-based clusters using only 5 major variables that has 87% accuracy compared with original imaging-based clusters using 57 variables. Journal of Allergy and Clinical Immunology 2017 140, 690-700.e8DOI: (10.1016/j.jaci.2016.11.053) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E1 A scree plot: eigenvalues (magnitudes of variances) according to the number of principal components for determining the optimal number of principal components. Journal of Allergy and Clinical Immunology 2017 140, 690-700.e8DOI: (10.1016/j.jaci.2016.11.053) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E2 Clustering analysis. A, Internal property in different clustering methods. B, Clustering stability analysis between K-means and hierarchical clustering with 4 or 5 numbers of clusters. C, Clustering membership of K-means clustering on 2-dimensional projected coordinates. D, Clustering membership of hierarchical clustering on 2-dimensional projected coordinates. Journal of Allergy and Clinical Immunology 2017 140, 690-700.e8DOI: (10.1016/j.jaci.2016.11.053) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions

Fig E3 Relationship of imaging-based clusters versus simple clinical clusters by Moore et al (Spearman correlation r = 0.319; P < .0001).E29 Journal of Allergy and Clinical Immunology 2017 140, 690-700.e8DOI: (10.1016/j.jaci.2016.11.053) Copyright © 2017 American Academy of Allergy, Asthma & Immunology Terms and Conditions