DOI: https://doi.org/10.21203/rs.3.rs-1272026/v1
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.
Methods 63 children with MPP (n=30) or SPP (n=33) with similar consolidation and surrounding halo on CT images in Qilu Hospital and Qilu Children's Hospital between January 2017 and July 2019 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 set (n=43) and the validation set (n=20) were established by stratified random sampling at a ratio of 7:3. By means of variance threshold, the effective radiomics features, Select Best and least absolute shrinkage and selection operator (LASSO) regression method were employed for feature selection. 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 was estimated and compared using the area under the receiver operating characteristic (ROC) curve (AUC), and the accuracy (score) matrix was established to compare and evaluate the results of different radiomics models.
Results XGBoost 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 set. The AUC value of MPP in validation set was 0.889, the sensitivity and specificity were 0.89 and 0.82, respectively; and the AUC value of SPP validation set was 0.889, the sensitivity and specificity were 0.82 and 0.89, respectively.
Conclusion The XGBoost 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.