ArticleFrontiers in medicine2026
Incremental predictive value of a CT-based deep learning radiomics model for differentiating benign and malignant pleural effusions.
Article in Frontiers in medicine, 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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Abstract
Objective: To develop and validate a diagnostic model for malignant (MPE) and benign pleural effusion (BPE) using non-contrast chest CT deep learning (DL) and radiomics features, and to explore its incremental value alongside conventional biochemical biomarkers. Methods: We retrospectively enrolled 208 patients (Jan 2020-Sep 2024) as internal training/testing cohorts (7:3 ratio) and 52 patients (Oct 2024-Dec 2025) for internal temporal validation. Radiomics and DL features were extracted from non-contrast CTs to construct a radiomics score (Radscore). Multivariable logistic regression evaluated the Radscore's independent predictive value. Net reclassification improvement (NRI) and integrated discrimination improvement (IDI) assessed the incremental diagnostic value when combined with clinical biomarkers. Results: Four radiomics features were ultimately retained to construct the Radscore. Multivariable adjusted analysis confirmed that the Radscore was an independent risk factor for MPE (OR: 2.718-2.776, Conclusion: The DL-radiomics-based Radscore is a promising quantitative biomarker for differentiating MPE from BPE. It functions independently of conventional biochemical metrics and provides meaningful incremental value, refining the accuracy of risk probability estimation in patients with pleural effusion.
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