Systemic sclerosis & Raynaud's
Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis
Authors' conclusion
Quantitative BCA biomarkers offer a means to predict survival and individual disease manifestations, in part outperforming established parameters.
Study context
- Population
- Systemic sclerosis (SSc) patients with at least one CT exam, from a prospectively maintained cohort of 452 SSc patientsfrom cohort of 452
- Participants
- 128 patients
- Exposure
- CT-derived body composition analysis (BCA) parameters vs BMI and clinical parameters for predicting survival
- Outcome
- Prediction of survival
Methodological detailsprospectively maintained cohort, AI-based 3D body composition analysis, K-means cluster analysis, longitudinal regression analyses
- Prospectively maintained cohort
- AI-based 3D body composition analysis
- K-means cluster analysis
- Longitudinal regression analyses
Main finding
As reported in the abstract
Survival prediction
| Group | AUC |
|---|---|
| BCA model | 0.75 |
BCA model
AUC0.75
Observational data: associations, not effects.
Further findings
Survival prediction by longitudinal development of the cardiac marker
- AUC
- 0.82
Complications, incl. ILD, in patients with altered BCA parameters
- Increased ORs for various complications, including ILD
- P < 0.05
BCA phenotypes
- Two distinct BCA phenotypes with significant differences in gastrointestinal disease manifestations
- P < 0.01
Original publication
Computed tomography-derived quantitative imaging biomarkers enable the prediction of disease manifestations and survival in patients with systemic sclerosis. RMD Open. Published 25 June 2025. doi:10.
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