Development of a radiomics-based model using computed tomography imaging to assess the incidence of extrapulmonary organ involvement in

Objectives: This study aimed to develop a predictive model, based on radiomics, to assess the occurrence of extrapulmonary organ involvement and predict recovery durations in children with Materials and methods: We retrospectively included 556 confirmed MPP patients from three medical centers between October 2022 and December 2024. Feature parameters were selected and weighted using Z-score normalization and LASSO. A logistic regression model was constructed to assess extrapulmonary organ involvement. Model performance was evaluated using the area under the curve (AUC), calibration curves, and decision curves, with comparisons between models conducted using the DeLong test. For predicting recovery duration, a separate model was developed based on selected features and was evaluated using mean squared error (MSE), the coefficient of determination (R Results: In the evaluation of the extrapulmonary organ involvement model, the Radiomics Model showed statistically significant differences when compared with both the Clinical Laboratory Model [(AUC = 0.73; 95% CI, 0.53-0.88) vs. (AUC = 0.67; 95% CI, 0.49-0.84), Conclusion: This study demonstrated that incorporating radiomics significantly improved the predictive accuracy of clinical laboratory parameters and imaging features for assessing extrapulmonary organ involvement and forecasting recovery durations in MPP patients. This approach provided an effective tool to enhance diagnostic efficiency for clinicians.