Differentiating COPD and Asthma using Quantitative CT Imaging and Machine Learning

Abstract

There are similarities and differences between chronic obstructive pulmonary disease (COPD) and asthma patients in terms of computed tomography (CT) disease-related features. Our objective was to determine the optimal subset of CT imaging features for differentiating COPD and asthma using machine learning.

COPD and asthma patients were recruited from Heidelberg University Hospital. CT was acquired and 93 features were extracted (VIDA Diagnostics): percentage of low-attenuating-areas below −950HU (LAA₉₅₀), LAA₉₅₀ hole count, estimated airway-wall-thickness for a 10 mm internal perimeter airway (Pi10), total-airway-count (TAC), as well as inner/outer perimeter/areas and wall thickness for each of five segmental airways, and the average of those five airways. Hybrid feature selection was used to select the optimum number of features, and support vector machine was used to classify COPD and asthma.

Ninety-five participants were included (n=48 COPD; n=47 asthma); there were no differences between COPD and asthma for age (p=0.25) or FEV₁ (p=0.31). In a model including all CT features, the accuracy and F1-score was 80% and 81%, respectively. The top features were: LAA_950, LAA₉₅₀ hole count, average outer and inner airway perimeter, outer and inner airway area RB1, and TAC. In the model with only airway features, the accuracy and F1-score were 66% and 68%, respectively. The top features were: inner area RB1, wall thickness RB1, outer area LB1, TAC LB10, average outer/inner perimeter, Pi10, and TAC.

In conclusions, COPD and asthma can be differentiated using machine learning with moderate-high accuracy by a subset of only 7 CT features.