PT - JOURNAL ARTICLE AU - Janine Schniering AU - Malgorzata Maciukiewicz AU - Hubert S. Gabrys AU - Matthias Brunner AU - Christian Blüthgen AU - Chantal Meier AU - Sophie Braga-Lagache AU - Anne-Christine Uldry AU - Manfred Heller AU - Matthias Guckenberger AU - Håvard Fretheim AU - Christos T. Nakas AU - Anna-Maria Hoffmann-Vold AU - Oliver Distler AU - Thomas Frauenfelder AU - Stephanie Tanadini-Lang AU - Britta Maurer TI - Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis AID - 10.1183/13993003.04503-2020 DP - 2022 May 01 TA - European Respiratory Journal PG - 2004503 VI - 59 IP - 5 4099 - http://erj.ersjournals.com/content/59/5/2004503.short 4100 - http://erj.ersjournals.com/content/59/5/2004503.full SO - Eur Respir J2022 May 01; 59 AB - Background Radiomic features calculated from routine medical images show great potential for personalised medicine in cancer. Patients with systemic sclerosis (SSc), a rare, multiorgan autoimmune disorder, have a similarly poor prognosis due to interstitial lung disease (ILD). Here, our objectives were 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 1355 robust radiomic features from standard-of-care CT images. We performed unsupervised clustering to identify and characterise imaging-based patient clusters. A clinically applicable prognostic quantitative radiomic risk score (qRISSc) for progression-free survival (PFS) was derived from radiomic profiles using supervised analysis. The biological basis of qRISSc was assessed in a cross-species approach by correlation with lung proteomic, 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 (qRISSc) composed of 26 features that accurately predicted PFS 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.CT-based radiomics decodes phenotypic, prognostic and molecular differences in SSc-ILD, and predicts progression-free survival with a significant impact on future clinical decision making in SSc-ILD https://bit.ly/3zPaMOn