Systemic sclerosis & Raynaud's

Automated high-resolution CT analysis outperforms visual assessment in predicting interstitial lung disease progression in SSc

Rheumatology (Oxford)Published Retrospective longitudinal study

Authors' conclusion

Automated quantitative HRCT analysis, but not visual evaluation, predicts long-term functional progression in SSc-ILD.

Study context

Population
Patients with SSc-ILD with high-resolution CT (HRCT) and pulmonary function tests (PFTs) at baseline (T1) and after 1 year (T2), PFTs repeated 12 months after T2 (T3)
Participants
33 patients
Exposure
Automated quantitative HRCT analysis vs visual assessment
Outcome
Identification of early SSc-ILD progression and prediction of subsequent functional decline (Erice/INBUILD criteria)
Methodological detailsblinded semi-quantitative visual scoring by two radiologists, automated analysis (Thoracic VCAR)
  • Blinded semi-quantitative visual scoring by two radiologists
  • Automated analysis (Thoracic VCAR)

Main finding

As reported in the abstract

Prediction of functional decline at T3 by early (T1-T2) automatically-detected GGO increase
Early (T1-T2) automatically-detected ground-glass opacity (GGO) increases predicted subsequent functional decline at T3
P = 0.04
GGO changes >50 ml (8.2%): sensitivity
81%
GGO changes >50 ml (8.2%): specificity
77%

Observational data: associations, not effects.

Further findings

Fibrosis progression T1 → T2

Visual: 1.5 → 1.6
P = 0.0005
Software: 0.34% → 0.4%
P = 0.01

GGO and normal lung tissue (automated only) · T2

GGO increase 15.5% → 15.8%
P = 0.04
Normal lung tissue reduced 75.6% → 73%
P = 0.009
  • Only automated analyses identified these changes

Correlation with PFT changes

Software-derived parameters correlated strongly with PFT changes; visual scores showed weaker correlations

Original publication

Automated high-resolution CT analysis outperforms visual assessment in predicting interstitial lung disease progression in SSc. Rheumatology (Oxford). Published 9 July 2026. doi:10.1093/rheumatology/keag346

Composed by AI from the published abstract and checked against it. How Briefs are made →

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