AUTHOR=Wang Dongdong , Zhao Jianshe , Zhang Ran , Yan Qinghu , Zhou Lu , Han Xiaoyu , Qi Yafei , Yu Dexin TITLE=The value of CT radiomic in differentiating mycoplasma pneumoniae pneumonia from streptococcus pneumoniae pneumonia with similar consolidation in children under 5 years JOURNAL=Frontiers in Pediatrics VOLUME=10 YEAR=2022 URL=https://www.frontiersin.org/journals/pediatrics/articles/10.3389/fped.2022.953399 DOI=10.3389/fped.2022.953399 ISSN=2296-2360 ABSTRACT=Objective

To 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.

Methods

A total of 102 children with MPP (n = 52) or SPP (n = 50) 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, SelectKBest 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 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.

Results

RF outperformed other classifiers and was selected as the backbone in the classifier with the consolidation + the surrounding halo was taken as ROI to differentiate MPP from SPP in validation cohort. The AUC value of MPP in validation cohort was 0.822, the sensitivity and specificity were 0.81 and 0.81, respectively.

Conclusion

The 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.