Lung ultrasound radiomics for identifying severe

Background: Methods: This retrospective study included children younger than 14 years with MPP between July 2020 and June 2025. Participants were allocated by stratified random sampling to training and held-out internal testing cohorts at an 8:2 ratio. Clinical data were collected within 24 h after admission, and lung ultrasound was performed within 72 h. Radiomics features were extracted from manually segmented pulmonary consolidations using PyRadiomics and selected by interobserver reproducibility assessment, correlation analysis, and least absolute shrinkage and selection operator regression. Clinical, Radiomics, and Integrated random forest models were developed, with hyperparameters optimized in the training cohort using stratified five-fold cross-validation. Model discrimination, calibration, clinical utility, and interpretability were evaluated using receiver operating characteristic analysis, calibration curves, decision curve analysis, pairwise comparisons, and SHapley Additive exPlanations (SHAP). Results: Overall, 530 children were included: 424 in the training cohort and 106 in the testing cohort. Training AUCs for the Clinical, Radiomics, and Integrated models were 0.785 (95% CI, 0.740-0.826), 0.794 (95% CI, 0.748-0.836), and 0.823 (95% CI, 0.780-0.863), respectively. Corresponding testing AUCs were 0.747 (95% CI, 0.646-0.837), 0.787 (95% CI, 0.683-0.877), and 0.801 (95% CI, 0.699-0.889). Although the Integrated model achieved the highest testing AUC, its improvement over the Radiomics model was small ( Conclusions: The Integrated model showed the highest discrimination, but a definite incremental benefit over the Radiomics model was not demonstrated. Lung ultrasound radiomics may provide a non-invasive quantitative approach for identifying SMPP in children; however, prospective multicenter external validation is required before clinical implementation.