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 l

SUBMITTER: Choi S 

PROVIDER: S-EPMC5534190 | biostudies-literature | 2017 Sep

REPOSITORIES: biostudies-literature