The Value Of CT Radiomic In Differentiating Mycoplasma Pneumoniae Pneumonia From Streptococcus Pneumoniae Pneumonia With Similar Consolidation In Children

ObjectivesTo investigate the value of CT radiomics in the differentiation of mycoplasma pneumoniae pneumonia(MPP)from streptococcus pneumoniae pneumonia(SPP)with similar CT manifestations in children under 5 years.Materials and Methods102 children with MPP or SPP with similar consolidation and surrounding halo on CT images in Qilu Hospital and Qilu Children’s Hospital between January 2017 and March 2022 were enrolled in the retrospective study.Radiomic features of the both lesions on plain CT images were extracted including the consolidation part of the pneumonia or both consolidation and surrounding halo area which were respectively delineated at region of interest(ROI)areas on the maximum axial image.The training cohort(n=71)and the validation cohort(n=31)were established by stratified random sampling at a ratio of 7:3.By means of variance threshold,the effective radiomics features,SelectBest and least absolute shrinkage and selection operator(LASSO)regression method were employed for feature selection and combined to calculate the radiomics score(Rad-score).Six classifiers,including k-nearest neighbor(KNN),support vector machine(SVM),extreme gradient boosting(XGBoost),random forest(RF),logistic regression(LR),and decision tree(DT)were used to construct the models and nomogram based on radiomic features.The diagnostic performance of these models and the radiomic nomogram was estimated and compared using the area under the receiver operating characteristic(ROC)curve(AUC),and The decision curve analysis(DCA)was used to evaluate which model achieved the most net benefit.ResultsWhen taking the consolidation region as the ROI,XGBoost classifier gets the highest score,and the results are as follows:In the test set,the AUC value was 0.563,95%confidence interval was 0.395-0.731,sensitivity was 0.470,specificity was 0.690,accuracy was 0.580,recall rate was 0.470,and F1 score was 0.520.When taking the consolidation region+the surrounding halo as ROI,RF classifier scored the highest,and the results were as follows:In the test set,the AUC value of RF classifier was 0.822,the 95%confidence interval was 0.684-0.960,the sensitivity was 0.810,the specificity was 0.810,the accuracy was 0.810,the recall rate was 0.810,and the F1 score was 0.810.ConclusionsThe RF model has the best classification efficiency in the identification of MPP from SPP in children,and the ROI with both consolidation and surrounding halo is most suitable for the delineation.