ArticleThe European respiratory journal2022
Computed tomography-based radiomics decodes prognostic and molecular differences in interstitial lung disease related to systemic sclerosis.
Article in The European respiratory journal, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 60 papers, 1 of them a synthesis that pooled it.
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60 citing papers in PubMed, 1 synthesis or guideline pooled it, 87 citations in OpenAlex.
- A systematic review of the role of quantitative CT in the prognostication and disease monitoring of interstitial lung disease.European respiratory review : an official journal of the European Respiratory Society · 2025Pooled it
- High-resolution computed tomography assessment of connective tissue disease-associated interstitial lung disease: Advances in diagnosis and prognostic evaluation.World journal of radiology · 2026Review
- Radiomics analysis and classification of esophageal involvement in systemic sclerosis using chest CT images.International journal of computer assisted radiology and surgery · 2026Article
- Integrated imaging-informed and machine learning-based prognostic models for risk stratification in connective tissue disease-associated interstitial lung disease: a multicenter study.Clinical rheumatology · 2026Article
- Lobar-level radiomic clustering reveals background lung changes associated with lung cancer risk: a new perspective for early screening.Insights into imaging · 2026Article
- Molecular imaging of fibroblast activation in systemic sclerosis using [European journal of nuclear medicine and molecular imaging · 2026Article
- Evolving landscape of imaging-based evaluation in systemic autoimmune rheumatic disease-associated interstitial lung disease: from visual assessment to quantitative artificial intelligence-assisted evaluation.Journal of rheumatic diseases · 2026Review
- Early detection and risk stratification in autoimmune-related interstitial lung disease: a state-of-the-art review.Respiratory research · 2026Review
- Development of imaging protocol and radiomics-based nomogram for assessing lesion reversibility in connective tissue disease-associated interstitial lung disease.BMC medical imaging · 2026Article
- Radiomics-based machine learning model for predicting secondary decompressive craniectomy in TBI patients after emergent craniotomy with bone flap replacement.Chinese neurosurgical journal · 2026Article
- Multi-omics to study chronic respiratory diseases and viral infections.European respiratory review : an official journal of the European Respiratory Society · 2026Review
- Machine learning for predicting acute exacerbation and mortality in idiopathic inflammatory myopathy-associated interstitial lung disease.Frontiers in medicine · 2026Article
- Personalized Medicine for Systemic Sclerosis-Associated Interstitial Lung Disease.Current treatment options in rheumatology · 2025Article
- Pulmonary Fibrosis-Focusing on the Future: Aspen Lung Conference 2024 Summary.American journal of respiratory cell and molecular biology · 2025Article
- Harnessing artificial intelligence to advance insights in systemic sclerosis skin and lung disease.Current opinion in rheumatology · 2025Review
- Common genetic variation influencing the human lung imaging phenotypes.Nature communications · 2025Article
- CT-based radiomics models decode fibrosis content and molecular differences in pancreatic ductal adenocarcinoma: a multi-institutional study.Insights into imaging · 2025Article
- Development and validation of a diagnostic radiomics model for the differential diagnosis of coexistent tuberculosis and lung cancer versus isolated lung cancer.Journal of thoracic disease · 2025Article
- Bi-Regional Machine Learning Radiomics Based on CT Noninvasively Predicts LOX Expression Level and Overall Survival in Hepatocellular Carcinoma.Cancer medicine · 2025Article
- Harnessing Radiomics and Explainable AI for the Classification of Usual and Nonspecific Interstitial Pneumonia.Journal of clinical medicine · 2025Article
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Authors and funding
17 authors at 3 institutions in 4 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiomic 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.
methodsWe 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.
resultsRadiomic 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.
conclusionsRadiomics-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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