{
  "abstract": "BackgroundAirway remodeling is a convergent feature across respiratory diseases, yet current quantitative CT tools quantify a small number of prespecified structural abnormalities of the airway tree. 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.\n\nMethodsRadAr 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.\n\nResultsIn fILD, lower-lobe architectural distortion was associated with mortality (balanced accuracy 0{middle dot}654). In COVID-19, severe disease was independently associated with luminal dilation (AUC 0{middle dot}719, odds ratio 2{middle dot}32, p=0{middle dot}017). In PPF, airway phenotypes correlated with forced vital capacity ({rho}=0{middle dot}83), mid-expiratory flow ({rho}=0{middle dot}87), and {superscript 1}{superscript 2}Xe MRI alveolar gas exchange impairment ({rho}=0{middle dot}70). In pediatric CF, reduced tapering and increased cylindricity were associated with prior exacerbations and bronchoalveolar lavage neutrophilia ({rho}=-0{middle dot}64 to -0{middle dot}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.\n\nConclusionRadAr 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.",
  "abstract_shown": true,
  "affiliations": [
    "Emory University"
  ],
  "article_type": "PUBLISHAHEADOFPRINT",
  "arxiv": null,
  "author_total": 11,
  "authors": [
    {
      "name": "P. Mutha"
    },
    {
      "name": "J. Lee"
    },
    {
      "name": "G. L. Silva"
    },
    {
      "name": "B. Dreihuys"
    },
    {
      "name": "Z. Healy"
    },
    {
      "name": "D. Mummy"
    },
    {
      "name": "B. Kaul"
    },
    {
      "name": "S. Ram"
    },
    {
      "name": "R. Tirouvanziam"
    },
    {
      "name": "L. Guglani"
    },
    {
      "affs": [
        0
      ],
      "name": "A. Madabhushi"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/48emvtjxj7w6e.bib",
  "cited_by": 0,
  "doi": "10.64898/2026.07.19.26358441",
  "fulltext_url": "https://www.medrxiv.org/content/10.64898/2026.07.19.26358441v2.full",
  "id": "48emvtjxj7w6e",
  "issns": [],
  "landing_url": "https://www.medrxiv.org/content/10.64898/2026.07.19.26358441v2",
  "language": null,
  "licence": "CC-BY-NC",
  "licence_label": "CC BY-NC",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by-nc/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "medrxiv",
      "respiratory medicine"
    ],
    [
      "openalex",
      "2741|Radiomics and Machine Learning in Medical Imaging"
    ]
  ],
  "openalex": "W7169876196",
  "pdf_url": "https://www.medrxiv.org/content/10.64898/2026.07.19.26358441v2.full.pdf",
  "pmcid": null,
  "pmid": null,
  "published": "2026-07-19",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/48emvtjxj7w6e.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:30Z",
      "native_id": "10.64898/2026.07.19.26358441",
      "source": "medrxiv",
      "url": "https://www.medrxiv.org/content/10.64898/2026.07.19.26358441v2"
    }
  ],
  "subjects": [
    {
      "field": "medicine",
      "origin": "openalex:2741|Radiomics and Machine Learning in Medical Imaging",
      "subfield": "imaging"
    }
  ],
  "title": "Radiomics of the Airway (RadAr): Multi-Scale Airway Phenotyping for Disease Characterization on Routine CT Imaging",
  "title_text": "Radiomics of the Airway (RadAr): Multi-Scale Airway Phenotyping for Disease Characterization on Routine CT Imaging",
  "type": "preprint",
  "updated_at": "2026-10-30T12:17:30Z",
  "url": "https://research.draconitic.com/paper/48emvtjxj7w6e",
  "venue": "medRxiv",
  "versions": []
}