Prognostication in patients with idiopathic pulmonary fibrosis using quantitative airway analysis from HRCT: a retrospective study.
Authors
Affiliations (21)
Affiliations (21)
- Bioengineering Department and Imperial-X, Imperial College London, London, UK [email protected].
- Royal Brompton Hospital, London, UK.
- Equal contribution.
- National Heart and Lung Institute, Imperial College London, London, UK.
- Institute of Cardiovascular Science, University College London, London, UK.
- Prince Charles Hospital, The University of Queensland, Australia.
- Department of Respiratory Medicine, John Hunter Hospital, New South Wales, Australia.
- Royal Prince Alfred Hospital, The University of Sydney, Camperdown, Australia.
- The Austin Hospital, The University of Melbourne, Melbourne, Australia.
- Royal Adelaide Hospital, The University of Adelaide, Adelaide, Australia.
- Sir Charles Gairdner Hospital, The University of Western Australia, Australia.
- Fiona Stanley Hospital; University of Western Australia.
- Allergy and Lung Health Unit, School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia.
- Department of Radiology, Alfred Health, Melbourne, Victoria, Australia.
- Department of Respiratory Medicine, Princess Alexandra Hospital, Brisbane, Queensland, Australia.
- Hervey Bay Hospital, Queensland & University of Queensland.
- NHMRC Centre of Research Excellence in Pulmonary Fibrosis, Sydney, Australia.
- Respiratory Medicine, Alfred Hospital, Melbourne, Australia.
- Bioengineering Department and Imperial-X, Imperial College London, London, UK.
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
- Equal senior co-last authors.
Abstract
Predicting shorter life expectancy is crucial for prioritizing antifibrotic therapy in fibrotic lung diseases, where progression varies widely, from stability to rapid deterioration. This heterogeneity complicates treatment decisions, emphasizing the need for reliable baseline measures. This study focuses on leveraging artificial intelligence model to address heterogeneity in disease outcomes, focusing on mortality as the ultimate measure of disease trajectory. This retrospective study included 1744 anonymised patients who underwent high-resolution CT scanning. The AI model, SABRE (Smart Airway Biomarker Recognition Engine), was developed using data from patients with various lung diseases (n=460, including lung cancer, pneumonia, emphysema, and fibrosis). Then, 1284 high-resolution CT scans with evidence of diffuse FLD from the Australian IPF Registry and OSIC were used for clinical analyses. Airway branches were categorized and quantified by anatomic structures and volumes, followed by multivariable analysis to explore the associations between these categories and patients' progression and mortality, adjusting for disease severity or traditional measurements. Cox regression identified SABRE-based variables as independent predictors of mortality and progression, even adjusting for disease severity (fibrosis extent, traction bronchiectasis extent, and ILD extent), traditional measures (FVC%, DLCO%, and CPI), and previously reported deep learning algorithms for fibrosis quantification and morphological analysis. Combining SABRE with DLCO significantly improved prognosis utility, yielding an AUC of 0.852 at the first year and a C-index of 0.752. SABRE-based variables capture prognostic signals beyond that provided by traditional measurements, disease severity scores, and established AI-based methods, reflecting the progressiveness and pathogenesis of the disease.