Schniering, Janine; Maciukiewicz, Malgorzata; Gabrys, Hubert S; Brunner, Matthias; Blüthgen, Christian; Meier, Chantal; Braga-Lagache, Sophie; Uldry, Anne-Christine; Heller, Manfred; Guckenberger, Matthias; Fretheim, Håvard; Nakas, Christos T.; Hoffmann-Vold, Anna-Maria; Distler, Oliver; Frauenfelder, Thomas; Tanadini-Lang, Stephanie; Maurer, Britta (2022). Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis. European respiratory journal, 59(5) European Respiratory Society 10.1183/13993003.04503-2020
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BACKGROUND
Radiomic 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).
OBJECTIVES
To explore computed tomography (CT)-based high-dimensional image analysis (radiomics) for disease characterisation, risk stratification, and relaying information on lung pathophysiology in SSc-ILD.
METHODS
We 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.
RESULTS
Radiomic 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.
CONCLUSIONS
Radiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision-making in SSc-ILD.
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