Radiomics of the Airway (RadAr): Multi-Scale Airway Phenotyping for Disease Characterization on Routine CT Imaging.

Authors

Pushkar Mutha, Juyoung Lee, George Lucas Silva, Bastiaan Dreihuys, Zachary Healy, David Mummy, Bhavika Kaul, Sundaresh Ram, Rabindra Tirouvanziam, Lokesh Guglani, Anant Madabhushi

Published in

medRxiv : the preprint server for health sciences. Jul 21, 2026. Epub Jul 21, 2026.

Abstract

Airway remodeling is a convergent feature across respiratory diseases, yet current CT tools provide limited characterization of the airway tree. We present Radiomics of the Airway (RadAr), an automated framework for multi-scale airway phenotyping from routine chest CT.
RadAr extracts multi-scale, interpretable airway measurements capturing luminal dimensions, tapering, architectural distortion, and global morphology and provides an interactive web portal for analysis and visualization. It was evaluated across four settings: 63-week mortality prediction in fibrotic interstitial lung disease (fILD; N=147), COVID-19 severity prediction (N=1164), structure-function association in progressive pulmonary fibrosis (PPF; N=9) and structure-inflammation markers in pediatric cystic fibrosis (CF; N=11). Unsupervised clustering identified airway phenotypes across the fILD and COVID-19 cohorts.
In fILD, lower-lobe architectural distortion was associated with mortality (balanced accuracy 0.654). In COVID-19, severe disease was independently associated with luminal dilation (AUC 0.719, odds ratio 2.32, p=0.017). In PPF, airway phenotypes correlated with forced vital capacity (ρ=0.83), mid-expiratory flow (ρ=0.87), and ¹²⁹Xe MRI alveolar gas exchange impairment (ρ=0.70). In pediatric CF, reduced tapering and increased cylindricity were associated with prior exacerbations and bronchoalveolar lavage neutrophilia (ρ=-0.64 to -0.78). Five phenotypes were identified from extensive, tapered airway trees to sparse, dilated, thick-walled, tortuous trees, with increasing COVID-19 severity and fILD mortality across this spectrum.
RadAr identified interpretable, disease-specific airway signatures associated with function and outcomes across restrictive, obstructive, and mixed lung diseases in adult and pediatric settings. It provides a scalable framework that may support diagnosis, risk stratification, and longitudinal monitoring across pulmonary diseases.

PMID:
42538919
Bibliographic data and abstract were imported from PubMed on 01 Aug 2026.

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