CT-based radiomics nomogram for distinguishing

Background: Methods: A total of 387 lesions of 325 children with pneumonia were retrospectively analyzed, including 162 MPP and 225 other pneumonias. The radiomic features were extracted from volume of interest (VOI) manually delineated on CT. Through analysis of variance (ANOVA) and least absolute shrinkage and selection operator (LASSO) regression with five-fold cross-validation, optimum radiomic features were screened out. Radiomics signature was calculated through the linear combinations of the screened features. A clinical model was established with clinical independent risk factors selected by univariate and multivariate logistic regression. A radiomics nomogram combining Rad-score and clinical independent risk factors was developed by multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were employed to evaluate the performance of the radiomics nomogram. Results: Fourteen radiomic features were selected to calculate the Rad-score. White blood cell (WBC) count and lymphocyte count were independent clinical risk factors. The radiomics nomogram showed good discrimination for the type of pneumonia in the training set with an area under the curve (AUC) of 0.874 [95% confidence interval (CI): 0.836-0.908] and in the testing set with an AUC of 0.841 (95% CI: 0.771-0.893). The DCA and calibration curve demonstrated that the radiomics nomogram had excellent consistency and clinical practicability. Conclusions: The CT-based radiomics nomogram had good performance in distinguishing MPP from other pneumonias in children with CAP, which can serve as a reference for clinical decision-making.