Systemic sclerosis & Raynaud's
Deep-learning CT imaging algorithm to detect usual interstitial pneumonia pattern in patients with systemic sclerosis-associated interstitial lung disease: association with disease progression and survival
Authors' conclusion
A higher probability of a SOFIA-determined UIP pattern is associated with more advanced ILD, disease progression and worse survival, suggesting that it may be a useful prognostic marker in SSc-ILD.
Study context
- Population
- Patients with SSc-ILD with high resolution CT (HRCT) images, concomitant lung function tests and follow-up data
- Participants
- 522 patients
- Exposure
- SOFIA-determined usual interstitial pneumonia (UIP) probability categories
- Outcome
- Prognostic utility of SOFIA UIP probability: FVC decline and survival
Methodological detailsdeep-learning HRCT algorithm (SOFIA), Prospective Investigation of Pulmonary Embolism Diagnosis (PIOPED)-based UIP probability categories, mixed-effect model, Cox proportional hazards analysis
- Deep-learning HRCT algorithm (SOFIA)
- PIOPED-based UIP probability categories
- Mixed-effect model
- Cox proportional hazards analysis
Main finding
As reported in the abstract
Distribution of PIOPED-based UIP probability categories
- Higher UIP probability associated with decline in forced vital capacity (FVC), independently of age, gender, ethnicity, smoking history and baseline FVC or Goh et al. staging system
- P = 0.008
- Higher UIP probability associated with worse 15-year survival, independently of age, gender, ethnicity, smoking history and baseline FVC or Goh et al. staging system
- P = 0.001
Observational data: associations, not effects.
Original publication
Deep-learning CT imaging algorithm to detect usual interstitial pneumonia pattern in patients with systemic sclerosis-associated interstitial lung disease: association with disease progression and survival. Rheumatology (Oxford). Published 17 October 2024. doi:10.
Composed by AI from the published abstract and checked against it. How Briefs are made →