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

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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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).


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.


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.


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.


Radiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision-making in SSc-ILD.

Item Type:

Journal Article (Original Article)


04 Faculty of Medicine > Department of Dermatology, Urology, Rheumatology, Nephrology, Osteoporosis (DURN) > Clinic of Rheumatology, Clinical Immunology and Allergology
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > DCR Services > Core Facility Massenspektrometrie- und Proteomics-Labor
04 Faculty of Medicine > Department of Haematology, Oncology, Infectious Diseases, Laboratory Medicine and Hospital Pharmacy (DOLS) > Institute of Clinical Chemistry

UniBE Contributor:

Brunner, Matthias, Braga, Sophie Marie-Pierre, Uldry, Anne-Christine, Heller, Manfred, Nakas, Christos T., Maurer, Britta


600 Technology > 610 Medicine & health




European Respiratory Society




Karin Balmer

Date Deposited:

16 Nov 2021 16:18

Last Modified:

05 Dec 2022 15:54

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