dc.creator | Schniering J., Maciukiewicz M., Gabrys H.S., Brunner M., Blüthgen C., Meier C., Braga-Lagache S., Uldry A.-C., Heller M., Guckenberger M., Fretheim H., Nakas C.T., Hoffmann-Vold A.-M., Distler O., Frauenfelder T., Tanadini-Lang S., Maurer B. | en |
dc.date.accessioned | 2023-01-31T09:54:38Z | |
dc.date.available | 2023-01-31T09:54:38Z | |
dc.date.issued | 2022 | |
dc.identifier | 10.1183/13993003.04503-2020 | |
dc.identifier.issn | 13993003 | |
dc.identifier.uri | http://hdl.handle.net/11615/78854 | |
dc.description.abstract | 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. Copyright ©The authors 2022. | en |
dc.language.iso | en | en |
dc.source | The European respiratory journal | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85130767157&doi=10.1183%2f13993003.04503-2020&partnerID=40&md5=332404f82474116eb91deeda02f191d4 | |
dc.subject | animal | en |
dc.subject | complication | en |
dc.subject | diagnostic imaging | en |
dc.subject | human | en |
dc.subject | interstitial lung disease | en |
dc.subject | lung | en |
dc.subject | mouse | en |
dc.subject | pathology | en |
dc.subject | procedures | en |
dc.subject | prognosis | en |
dc.subject | proteomics | en |
dc.subject | systemic sclerosis | en |
dc.subject | x-ray computed tomography | en |
dc.subject | Animals | en |
dc.subject | Humans | en |
dc.subject | Lung | en |
dc.subject | Lung Diseases, Interstitial | en |
dc.subject | Mice | en |
dc.subject | Prognosis | en |
dc.subject | Proteomics | en |
dc.subject | Scleroderma, Systemic | en |
dc.subject | Tomography, X-Ray Computed | en |
dc.subject | NLM (Medline) | en |
dc.title | Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis | en |
dc.type | journalArticle | en |