Table 1_Lung ultrasound radiomics for identifying severe Mycoplasma pneumoniae pneumonia in children: a model development study.xlsx

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).</p>Results<p>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 (ΔAUC = 0.014) and not significant by the DeLong test (P = 0.246) or integrated discrimination improvement analysis (P = 0.288).

SHAP identified transformed grayscale intensity, texture organization, lesion morphology, and lactate dehydrogenase as important predictors.</p>Conclusions<p>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.</p>