Quantitative computed tomography-derived clusters: redefining airway remodeling in asthmatic patients

Abstract

Background: Asthma heterogeneity is multidimensional and requires additional tools to unravel its complexity. Computed tomography (CT)-assessed proximal airway remodeling and air trapping in asthmatic patients might provide new insights into underlying disease mechanisms.

Objectives: The aim of this study was to explore novel, quantitative, CT-determined asthma phenotypes.

Methods: Sixty-five asthmatic patients and 30 healthy subjects underwent detailed clinical, physiologic characterization and quantitative CT analysis. Factor and cluster analysis techniques were used to determine 3 novel, quantitative, CT-based asthma phenotypes.

Results: Patients with severe and mild-to-moderate asthma demonstrated smaller mean right upper lobe apical segmental bronchus (RB1) lumen volume (LV) in comparison with healthy control subjects (272.3 mm(3) [SD, 112.6 mm(3)], 259.0 mm(3) [SD, 53.3 mm(3)], 366.4 mm(3) [SD, 195.3 mm(3)], respectively; P = .007) but no difference in RB1 wall volume (WV). Air trapping measured based on mean lung density expiratory/inspiratory ratio was greater in patients with severe and mild-to-moderate asthma compared with that seen in healthy control subjects (0.861 [SD, 0.05)], 0.866 [SD, 0.07], and 0.830 [SD, 0.06], respectively; P = .04). The fractal dimension of the segmented airway tree was less in asthmatic patients compared with that seen in control subjects (P = .007). Three novel, quantitative, CT-based asthma clusters were identified, all of which demonstrated air trapping. Cluster 1 demonstrates increased RB1 WV and RB1 LV but decreased RB1 percentage WV. On the contrary, cluster 3 subjects have the smallest RB1 WV and LV values but the highest RB1 percentage WV values. There is a lack of proximal airway remodeling in cluster 2 subjects.

Conclusions: Quantitative CT analysis provides a new perspective in asthma phenotyping, which might prove useful in patient selection for novel therapies.

Keywords: Asthma; CT; airway remodeling; cluster analysis; distal airway; fractal analysis; phenotypes; quantitative imaging.

Fig E1

Densitometric indices. A cumulative voxel distribution histogram identifying derivation of various densitometric indices is shown. Perc15, 15th Percentile point.

Fig E2

Standardization of densitometric indices. Regression equations were calculated from measurements of the densitometric standards (extrathoracic air, blood, and 3 electron density rods) for each CT scan of every subject in relation to the standard density measures of the densitometric standards. The regression equations derived were used to adjust all the densitometric indices for each CT scan of every subject. The figure illustrates derivation of regression equation for standardization of densitometric indices for one of the subjects.

Fig E3

Fractal analysis of the segmented airway tree with FracLac (ImageJ) software. The JPEG image (A) of the segmented airway tree is binarized (B) by ImageJ software by assigning black pixel color for the airway tree and white pixel color for background. The box-counting algorithm places several grids of decreasing box size (C and D) over the ROI (ie, binarized image of the tracheobronchial tree to determine the fractal dimension).

Fig E4

Fractal dimension of low attenuation areas in lungs. Coronal CT image showing areas of low attenuation less than the threshold CT attenuation value of −950 HU on inspiratory scans (A) and −850 HU on expiratory scans (C), with each lobe coded with a different color. Respective low attenuation clusters are shown in B and D, which are used to assess the size and distribution of emphysematous lesions and air-trapping areas, respectively.

Fig E5

Bland-Altman plots and intraobserver repeatability with PW2 software. Differences between measures of LA (A), WA (B), and length (C) by single observers 2 months apart (y-axis) are plotted against the average of LA (Fig E5, A), WA (Fig E5, B), and length (Fig E5, C) measurements (x-axis).

Fig E6

Accuracy of airway morphometry assessed by using PW2 software. Comparison of PW2 and stereomicroscopic measures of LA (A and B), WA (C and D), and length (E and F) by using the paired t test and Bland-Altman plots (difference [y-axis] vs the average [x-axis] measure using the 2 methods is plotted).

Fig E7

Quantitative CT in healthy subjects and patients with severe asthma. Three-dimensional reconstruction of an airway tree from a healthy subject (A) and a patient with severe asthma (B) using PW2 software, illustrating that in healthy subjects subsegmental airways can be reconstructed to more generations than in asthmatic patients. This is due to increased airway narrowing and closure in asthmatic patients. The insets illustrate the cross-section of RB1 in healthy subjects and patients with severe asthma, showing that the lumen is narrowed in asthmatic patients, with a decrease in total area and an increase in percentage WA but a relatively preserved WA. Coronal section of expiratory CT from a healthy subject (C) and a patient with severe asthma (D) showing the increased low attenuation areas (color coded according to lung lobes) in patients with severe asthma compared with healthy subjects.

Fig E8

Fractal dimension of the segmented airway tree on an inspiratory CT scan in asthmatic patients and healthy subjects (∗P < .05, ANOVA).

Fig 1

Proximal airway remodeling in asthma phenotypes. Pictures of segmented airway tree and RB1 CT cross-section (insets) of asthmatic patients from cluster 1 (A), cluster 2 (B), and cluster 3 (C) are shown.