{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T05:20:22Z","timestamp":1791004822977,"version":"4.1.0"},"reference-count":51,"publisher":"European Respiratory Society (ERS)","issue":"5","license":[{"start":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T00:00:00Z","timestamp":1634169600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"name":"OPO Foundation"},{"DOI":"10.13039\/501100008473","name":"Gebauer Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100008473","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Prof. Max Cloetta Foundation"},{"DOI":"10.13039\/501100006447","name":"Universit\u00e4t Z\u00fcrich","doi-asserted-by":"publisher","award":["Forschungskredit PostDoc FK-19-046"],"award-info":[{"award-number":["Forschungskredit PostDoc FK-19-046"]}],"id":[{"id":"10.13039\/501100006447","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013363","name":"Lunge Z\u00fcrich","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100013363","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005688","name":"Gottfried und Julia Bangerter-Rhyner-Stiftung","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005688","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["publications.ersnet.org"],"crossmark-restriction":true},"short-container-title":["Eur Respir J"],"accepted":{"date-parts":[[2021,9,23]]},"published-print":{"date-parts":[[2022,5]]},"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>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 (\u201cradiomics\u201d) for disease characterisation, risk stratification and relaying information on lung pathophysiology in SSc-ILD.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>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.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>Radiomics-based risk stratification using routine CT images provides complementary phenotypic, clinical and prognostic information significantly impacting clinical decision making in SSc-ILD.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1183\/13993003.04503-2020","type":"journal-article","created":{"date-parts":[[2021,10,14]],"date-time":"2021-10-14T12:26:51Z","timestamp":1634214411000},"page":"2004503","update-policy":"https:\/\/doi.org\/10.1183\/ers-crossmark-policy","source":"Crossref","is-referenced-by-count":90,"title":["Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis"],"prefix":"10.1183","volume":"59","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7869-4936","authenticated-orcid":false,"given":"Janine","family":"Schniering","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malgorzata","family":"Maciukiewicz","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hubert 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