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

Area of Science:

• Pulmonary Medicine
• Radiology
• Computational Biology

Background:

• Asthma is a complex, heterogeneous disease requiring advanced tools for deeper understanding.
• Computed tomography (CT)-assessed proximal airway remodeling and air trapping may offer new insights into asthma mechanisms.

Purpose of the Study:

• To explore novel, quantitative, CT-determined asthma phenotypes.
• To identify distinct patient clusters based on CT imaging characteristics.

Main Methods:

• Quantitative CT analysis was performed on 65 asthma patients and 30 healthy controls.
• Clinical and physiological data were collected for comprehensive characterization.
• Factor and cluster analysis techniques were employed to define novel asthma phenotypes.

Main Results:

• Asthma patients exhibited smaller mean right upper lobe apical segmental bronchus (RB1) lumen volume (LV) compared to controls (P = .007).
• Increased air trapping was observed in both severe and mild-to-moderate asthma patients (P = .04).
• Three distinct CT-based asthma clusters were identified, all characterized by air trapping, with varying degrees of airway remodeling and lumen/wall volume.

Conclusions:

• Quantitative CT analysis offers a novel perspective for asthma phenotyping.
• These CT-derived phenotypes may assist in selecting patients for targeted novel therapies.
• This approach enhances the understanding of asthma heterogeneity.