Correlation between the severity of asthma and computed tomography-based large airway structural changes among...

Area of Science:

• Pediatric Pulmonology
• Medical Imaging
• Computational Biology

Background:

• Pediatric asthma diagnosis lacks objective criteria, hindering early assessment and severity grading.
• Airway structural changes in asthmatic children may correlate with disease severity.
• Accurate assessment of asthma severity in young children remains a clinical challenge.

Purpose of the Study:

• To investigate the relationship between respiratory tract morphology and asthma severity in children under 3 years old.
• To explore the potential of computed tomography (CT) imaging features for predicting asthma severity in young children.
• To establish quantitative criteria for assessing pediatric asthma severity.

Main Methods:

• Retrospective analysis of low-dose computed tomography (CT) data from 244 children with asthma (<3 years old).
• Utilized a machine learning approach to analyze morphologic characteristics of major airways.
• Correlated airway features (wall thickness, ellipticity) with asthma severity classified by drug treatment response (moderate vs. severe).

Main Results:

• Children with severe asthma exhibited significantly greater airway wall thickness and ellipticity compared to those with moderate asthma.
• Airway morphologic data achieved an area under the receiver operating characteristic curve of 0.83 for classifying asthma severity.
• The machine learning model demonstrated a high accuracy (0.79) in differentiating moderate from severe asthma cases.

Conclusions:

• Morphologic characteristics of the respiratory tract are strongly associated with asthma severity in children under 3 years old.
• CT-based airway imaging analysis offers a promising objective tool for assessing pediatric asthma severity.
• These findings support the development of quantitative imaging biomarkers for early and accurate asthma diagnosis in infants and toddlers.