ArticleFrontiers in medicine2025
Multicenter study on CT-based Radiomics for predicting severity and delayed recovery in
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Development and internal validation of a CT radiomics-based model for severity classification in HIV-associatedJournal of thoracic disease · 2026Article
- Lung ultrasound radiomics for identifying severeFrontiers in pediatrics · 2026Article
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4 authors.
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Abstract
Objective: To develop and validate model based on clinical, imaging, and Radiomics features for predicting disease severity and delayed recovery in Methods: This multicenter retrospective study enrolled 238 patients (training cohort), 60 (testing cohort), and 278 (validation cohort). Patients were classified into non-severe MPP (NSMPP) and severe MPP (SMPP) groups based on guideline, and further stratified post-treatment into recovery or delayed recovery groups. Radiomics features were extracted from chest CT using PyRadiomics, with Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection. Three random forest-based predictive models were developed, including Clinical-Image, Radiomics, and Integrated. Predictive performance was evaluated via by the area under the receiver operating characteristic curve (AUC), calibration, and clinical utility. Results: The Integrated model demonstrated superior discrimination for severity prediction (validation AUC: 0.784, 95% CI: 0.722-0.845) and delayed recovery (validation AUC: 0.865, 95% CI: 0.770-0.960), outperforming Clinical-Image (severity AUC: 0.771, 95% CI: 0.695-0.847; delayed recovery AUC: 0.807, 95% CI: 0.724-0.950) and Radiomics model (severity AUC: 0.710, 95% CI: 0.643-0.776; delayed recovery AUC: 0.837, 95% CI: 0.724-0.950). Integrated Discrimination Improvement (IDI) analysis demonstrated significant enhancements in the Integrated model compared to both the Clinical-Image and Radiomics models for predicting both disease severity and delayed recovery (all Conclusion: The Integrated model combining clinical, imaging, and Radiomics features enhances risk stratification for MPP severity and delayed recovery.
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