Evidence map›Paper›PMID 42426553›Full record

ArticleRheumatology (Oxford, England)2026

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

Francesca Motta, Antonio Tonutti, Federica Catapano, Gianluca Sellaro, Francesco Amati, Anna Stainer, Calogero Messana, Stefano Aliberti, Marco Francone, Carlo Selmi and 1 more

Abstract readComparative Study
In one paragraph

Article in Rheumatology (Oxford, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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4 · The record

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5 · Who and what money

Authors and funding

11 authors.

Francesca MottaDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.ORCID 0000-0002-8093-2734
Antonio TonuttiDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Federica CatapanoDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Gianluca SellaroDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Francesco AmatiDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.ORCID 0000-0002-8248-7785
Anna StainerDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Calogero MessanaDepartment of Radiology, IRCCS Humanitas Research Hospital, Rozzano, Milan, Italy.
Stefano AlibertiDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.
Marco FranconeDepartment of Medical-Surgical Sciences and Translational Medicine, School of Medicine and Psychology, 'Sapienza'-University of Rome, Rome, Italy.ORCID 0000-0002-7906-3420
Carlo SelmiDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.ORCID 0000-0002-0323-0376
Maria De SantisDepartment of Biomedical Sciences, Humanitas University, Pieve Emanuele, Milan, Italy.ORCID 0000-0002-3196-1336

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesProgressive interstitial lung disease (ILD) is the leading cause of mortality in SSc. Early recognition of progression is crucial to timely treatment, but reliable early markers are lacking. Automated quantitative analysis of chest high-resolution CT (HRCT) may provide an objective tool to detect early progression. This study aims to compare automated and visual assessment in identifying early SSc-ILD progression and evaluate their predictive value for subsequent functional decline.

methodsIn this retrospective longitudinal study, 33 patients with SSc-ILD underwent HRCT and pulmonary function tests (PFTs) at baseline (T1) and after 1 year (T2; IQR 0.75-2.5 years). PFTs were repeated 12 months after T2 (T3; IQR 9-14.5 months, T3). Two radiologists performed blinded semi-quantitative visual scoring of fibrosis and ground-glass opacity (GGO), while automated analysis was conducted using Thoracic VCAR (GE Healthcare). Associations with PFTs and prediction of functional progression (Erice/INBUILD criteria) and treatment escalation were analysed.

resultsBoth visual and automated analyses detected fibrosis progression (visual: 1.5 → 1.6, P = 0.0005; software: 0.34% → 0.4%, P = 0.01). Only automated analyses identified increases in GGO (15.5% → 15.8%, P = 0.04) and reduced normal lung tissue (75.6% → 73%, P = 0.009). Software-derived parameters correlated strongly with PFT changes, whereas visual scores showed weaker correlations. Early (T1-T2) automatically-detected GGO increases predicted subsequent functional decline at T3 (P = 0.04), with changes >50 ml (8.2%) yielding 81% sensitivity and 77% specificity.

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

Indexed as

Lung Diseases, InterstitialScleroderma, SystemicTomography, X-Ray ComputedAgedDisease ProgressionFemaleHumansLongitudinal StudiesLungMaleMiddle AgedPredictive Value of TestsRespiratory Function TestsRetrospective Studiesartificial intelligenceautomated quantitative analysisinterstitial lung diseaseprogressionpulmonary function testsSSc

Identifiers

PMID42426553
PMCPMC13355593

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.