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Radiomics of the Airway (RadAr) derives interpretable, multi-scale airway measurements from chest CT to characterize airway deformation and discover quantitative imaging biomarkers from routine chest CT.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>RadAr extracts over 400 multi-scale, interpretable airway measurements across lobes or generations capturing luminal dimensions, tapering, architectural distortion, and global morphology. It was evaluated in N=1331 patients across four settings: 63-week mortality prediction in fibrotic interstitial lung disease (fILD), COVID-19 severity prediction, structure-function association in progressive pulmonary fibrosis (PPF) and structure-inflammation markers in pediatric cystic fibrosis (CF). Unsupervised clustering was used to identify airway phenotypes across the fILD and COVID-19 cohorts.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>In fILD, lower-lobe architectural distortion was associated with mortality (balanced accuracy 0\u00b7654). In COVID-19, severe disease was independently associated with luminal dilation (AUC 0\u00b7719, odds ratio 2\u00b732, p=0\u00b7017). In PPF, airway phenotypes correlated with forced vital capacity (\u03c1=0\u00b783), mid-expiratory flow (\u03c1=0\u00b787), and \u00b9\u00b2\u2079Xe MRI alveolar gas exchange impairment (\u03c1=0\u00b770). In pediatric CF, reduced tapering and increased cylindricity were associated with prior exacerbations and bronchoalveolar lavage neutrophilia (\u03c1=-0\u00b764 to -0\u00b778). 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.<\/jats:p>\n                <\/jats:sec>\n                <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>RadAr identified interpretable, disease-specific airway phenotypes associated with function and outcomes across restrictive, obstructive, and mixed lung diseases in adult and pediatric settings. These findings establish a framework for discovery and development of quantitative airway biomarkers for patient stratification, disease monitoring, or imaging endpoint development in pulmonary trials.<\/jats:p>\n                <\/jats:sec>","DOI":"10.64898\/2026.07.19.26358441","type":"posted-content","created":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T22:00:15Z","timestamp":1784671215000},"source":"Crossref","is-referenced-by-count":0,"title":["Radiomics of the Airway (RadAr): Multi-Scale Airway Phenotyping for Disease Characterization on Routine CT Imaging"],"prefix":"10.64898","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5961-7131","authenticated-orcid":false,"given":"Pushkar","family":"Mutha","sequence":"first","affiliation":[{"name":"Wallace H. 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