ArticleERJ open research2026
Radiomic analysis of computed tomography scans in COPD patients relates to different clinical and biological features, and identifies different lung function trajectories during follow-up.
Article in ERJ open research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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7 authors.
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Abstract
Background: COPD is a heterogeneous condition characterised by persistent, poorly reversible airflow obstruction. While some patients experience accelerated decline in forced expiratory volume in 1 s (FEV Methods: We analysed 101 radiomic features from 1759 chest CT scans of COPD patients in the ECLIPSE study. Unsupervised consensus clustering identified six mutually exclusive radiomic clusters, and we derived six corresponding average patient score clusters (APSC1-6). Random coefficient models assessed associations between each APSC and baseline clinical characteristics, FEV Results: Radiomic scores were associated with multiple baseline clinical features. Higher APSC2 (-5.3 mL·year Conclusions: Unsupervised CT radiomic analysis identifies distinct COPD endophenotypes associated with variability in FEV
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