Early recognition of necrotizing pneumonia in children based on non-contrast-enhanced computed tomography radiomics signatures

Background Necrotizing pneumonia (NP) is an infrequent but severe complication of pneumonia in children. In the early stages of NP, CT imaging shows lung consolidation, which cannot be detected in time. This study aimed to explore the ability of non-contrast-enhanced CT radiomics features to recognize NP in early stage. Methods This was a retrospective study, and 250 patients who presented with lung consolidation on initial CT images were included in this study. After a follow-up period of 1–3 weeks, 116 patients developed NP, whose CT or X-ray shows cavitation or liquefied necrosis. Manual segmentation of lesion sites in the initial non-contrast-enhanced CT scans was performed with RadCloud (Huiying Medical Technology Co., Ltd., China), and 1,409 radiomics features were extracted. We used Variance threshold (0.8), SelectKBest, and the least absolute shrinkage and selection operator (LASSO) methods for feature dimension reduction. Three machine learning algorithms, k-nearest neighbor (KNN), support vector machine (SVM), and logistic regression (LR) models, were established to recognize NP early. To assess the recognition performance, the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and other indicators were used in the validation cohort. Results Radiomics features helped to recognize NP in early stage in both the training and validation cohorts. The AUC (sensitivity, specificity) for the training and validation cohorts were 0.81 (0.73, 0.68) and 0.71 (0.61, 0.65) for KNN, respectively; 0.81 (0.72, 0.70) and 0.77 (0.66, 0.65) for SVM, respectively; and 0.82 (0.73, 0.73) and 0.76 (0.63, 0.70) for LR, respectively. Recall and F1-scores determined that LR performed better at diagnosing early NP, with the values of the above two indexes being 0.70 and 0.67, respectively. Conclusions Non-contrast-enhanced CT-based radiomics models may be helpful for recognizing NP in early stage.