AI-based HRCT quantification reveals DLCO and total lung capacity (TLC) as key determinants of interstitial lung disease (ILD) severity in connective tissue diseases
Authors' conclusion
The study presented that DLCO and TLC were predictive for the CTD-ILD severity.
Study context
- Population
- Patients with CTD-ILD who underwent pulmonary function test (PFT) and HRCTeligible for regression analysis
- Participants
- 76 patients
- Outcome
- Extent of ILD (GGO, reticulations, HAV, emphysema, overall extent) quantified by AI-based HRCT, and its association with clinical risk factors
Methodological detailsretrospective, regression analysis
- Retrospective
- Regression analysis
Main finding
As reported in the abstract
Clinical factors associated with ILD extent on AI-quantified HRCT
- Reduced DLCO, reduced TLC and elevated inflammation parameter significantly associated with extent of ground glass opacities (GGO), reticulations, HAV and overall ILD extent
- Pulmonary symptoms, age and FVC not associated with ILD extent
Observational data: associations, not effects.
Original publication
AI-based HRCT quantification reveals DLCO and TLC as key determinants of ILD severity in connective tissue diseases. RMD Open. Published 28 October 2025. doi:10.
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