Chest CT Biomarkers as Prognostic Predictors in SSc-ILD
NCT ID: NCT06472362
Last Updated: 2025-09-11
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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ENROLLING_BY_INVITATION
1000 participants
OBSERVATIONAL
2024-11-01
2026-03-31
Brief Summary
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Detailed Description
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In this study the prognostic potential and accuracy of machine-learning derived biomarkers to evaluate abnormalities that are difficult to quantify visually will be investigated. Whether novel high resolution computed tomography (HRCT) imaging biomarkers of airways, vessels, and overall extent of fibrosis at baseline can predict ILD progression, vasculopathy development, and survival will be investigated in a cohort of approximately 1,000 SSc-ILD patients.
The algorithm scores will be evaluated against survival using Cox proportional hazards modelling, while mixed effects model analysis will be used to assess links with change in lung function: forced vital capacity (FVC), diffusing capacity for carbon monoxide (DLco), and carbon monoxide transfer coefficient (Kco). The airway algorithm measuring traction bronchiectasis (dilatation of the airways due to surrounding fibrosis) may predict worsening of FVC, reflective of ILD progression. The vessel algorithm may predict decline in KCO, a marker of pulmonary vascular involvement. Exploratory analyses evaluating change in HRCT fibrosis extent over time for patients with repeat HRCTs will also be performed, and whether composite outcomes of change in HRCT and lung function variables improve long term outcome prediction and pave the way to their use in clinical trials and routine clinical use. Patients with trivial changes on CT will also be included to assess for very early changes that could be predictive of future decline. These algorithms will be combined with the findings of our previous study, which suggest that a certain type of pattern on CT called UIP predicts shorter survival.
Conditions
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Study Design
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COHORT
RETROSPECTIVE
Interventions
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HRCT biomarkers
HRCT imaging biomarkers of airways, vessels, and overall extent of fibrosis
Eligibility Criteria
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Inclusion Criteria
* ≥18 years old
* HRCT between 01/01/1990 and 31/12/2019
Exclusion Criteria
* \<18 years old
* lack of availability of HRCT imaging data
18 Years
99 Years
ALL
No
Sponsors
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Imperial College London
OTHER
Royal Free and University College Medical School
OTHER
The Leeds Teaching Hospitals NHS Trust
OTHER
Hannover Medical School
OTHER
University of Siena
OTHER
Università Politecnica delle Marche
OTHER
Azienda Ospedaliero Universitaria di Sassari
OTHER
Bichat Hospital
OTHER
Royal Brompton & Harefield NHS Foundation Trust
OTHER
Responsible Party
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Principal Investigators
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Elisabetta A Renzoni
Role: PRINCIPAL_INVESTIGATOR
Royal Brompton and Harefield Hospitals
Locations
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Bichat-Claude Bernard hospital
Paris, , France
Hanover Medical School
Hanover, , Germany
Marche Polytechnic University
Ancona, , Italy
Sassari University
Sassari, , Italy
Siena University Hospital
Siena, , Italy
Leeds Hospital/University of Leeds
Leeds, , United Kingdom
Royal Brompton Hospital
London, , United Kingdom
Countries
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Other Identifiers
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RBH2
Identifier Type: -
Identifier Source: org_study_id
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