Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis

Abstract

BackgroundRadiomic features calculated from routine medical images show great potential for personalized medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multi-organ autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD).ObjectivesTo explore computed tomography (CT)-based high-dimensional image analysis (radiomics) for disease characterisation, risk stratification, and relaying information on lung pathophysiology in SSc-ILD.MethodsWe investigated two independent, prospectively followed SSc-ILD cohorts (Zurich, derivation cohort, n=90; Oslo, validation cohort, n=66). For every subject, we defined 1′355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterize imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomics, histological and gene expression data derived from mice with bleomycin-induced lung fibrosis.ResultsRadiomic profiling identified two clinically and prognostically distinct SSc-ILD patient clusters. To evaluate the clinical applicability, we derived and externally validated a binary, quantitative radiomic risk score composed of 26 features, qRISSc, that accurately predicted progression-free survival and significantly improved upon clinical risk stratification parameters in multivariable Cox regression analyses in the pooled cohorts. A high qRISSc score, which identifies patients at risk for progression, was reverse translatable from human to experimental ILD and correlated with fibrotic pathway activation.ConclusionsRadiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision-making in SSc-ILD.