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

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.