Chest CT Biomarkers as Prognostic Predictors in SSc-ILD

Deep-learning Derived Chest Computed Tomography (CT) Biomarkers as Prognostic Predictors in Systemic Sclerosis Associated Interstitial Lung Disease (SSc-ILD)

The goal of this retrospective observational study is to investigate whether novel imaging biomarkers of airways, vessels, and overall extent of fibrosis at baseline predict ILD progression, vasculopathy development, and survival in SSc-ILD.

Study Overview

• Status: Enrolling by invitation
• Conditions: Systemic Sclerosis
• Intervention / Treatment: Diagnostic test: HRCT biomarkers

Detailed Description

Interstitial lung disease (ILD or lung fibrosis=stiffening of the lungs by scar tissue) develops in over half of patients with systemic sclerosis (SSc). Whilst ILD remains stable in some patients, at least a third have progressively increasing fibrosis. There is a pressing need for accurate indicators that identify a) patients at higher risk of progression, needing immediate treatment to prevent further irreversible ILD; and b) patients at lower risk, not needing treatment.

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.

• Study Type: Observational
• Enrollment (Estimated): 1000

Contacts and Locations

Study Locations

• France
  • Paris, France
    • Bichat-Claude Bernard Hospital
• Germany
  • Hanover, Germany
    • Hanover Medical School
• Italy
  • Ancona, Italy
    • Marche Polytechnic University
  • Sassari, Italy
    • Sassari University
  • Siena, Italy
    • Siena University Hospital
• United Kingdom
  • Leeds, United Kingdom
    • Leeds Hospital/University of Leeds
  • London, United Kingdom
    • Royal Brompton Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Probability Sample

Study Population

Secondary/tertiary care patients with SSc

Description

Inclusion Criteria:

• diagnosed with SSc
• ≥18 years old
• HRCT between 01/01/1990 and 31/12/2019

Exclusion Criteria:

• Patients who do not have SSc
• <18 years old
• lack of availability of HRCT imaging data

Study Plan

How is the study designed?

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Survival	Transplant-free survival	15 years
Pulmonary hypertension	Development of pulmonary hypertension	15 years
Decline in FVC	Change in lung function measure FVC	15 years
Decline in DLCO	Change in lung function measure DLCO	15 years
Decline in KCO	Change in lung function measure KCO	15 years

Collaborators and Investigators

• Sponsor: Royal Brompton & Harefield NHS Foundation Trust
• Collaborators: Hannover Medical School; Imperial College London; The Leeds Teaching Hospitals NHS Trust; Università Politecnica delle Marche; Bichat Hospital; University of Siena; Azienda Ospedaliero Universitaria di Sassari; Royal Free and University College Medical School
• Investigators: Principal Investigator: Elisabetta A Renzoni, Royal Brompton and Harefield Hospitals

Study record dates

Study Major Dates

• Study Start (Actual): November 1, 2024
• Primary Completion (Estimated): December 1, 2025
• Study Completion (Estimated): March 31, 2026

Study Registration Dates

• First Submitted: June 11, 2024
• First Submitted That Met QC Criteria: June 17, 2024
• First Posted (Actual): June 25, 2024

Study Record Updates

• Last Update Posted (Estimated): September 11, 2025
• Last Update Submitted That Met QC Criteria: September 4, 2025
• Last Verified: September 1, 2025

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Connective Tissue Diseases; Skin Diseases; Skin and Connective Tissue Diseases; Scleroderma, Systemic
• Other Study ID Numbers: RBH2

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No