ArticleTranslational pediatrics2021
Early recognition of necrotizing pneumonia in children based on non-contrast-enhanced computed tomography radiomics signatures.
Article in Translational pediatrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Development and validation of a clinical-radiomics nomogram for differentiatingFrontiers in pediatrics · 2026Article
- Semiquantitative chest computed tomography scoring system to estimate severity in pediatric community-acquired pneumonia.Frontiers in pediatrics · 2025Article
- Article
- Radiomics with structural magnetic resonance imaging, surface morphometry features, neurology scales, and clinical metrics to evaluate the neurodevelopment of preschool children with corrected tetralogy of Fallot.Translational pediatrics · 2024Article
- Identifying immunodeficiency status in children with pulmonary tuberculosis: using radiomics approach based on un-enhanced chest computed tomography.Translational pediatrics · 2023Article
- Radiomics-Assisted Computed Tomography-Based Analysis to Evaluate Lung Morphology Characteristics after Congenital Diaphragmatic Hernia.Journal of clinical medicine · 2023Article
- Necrotizing Pneumonia in Children: Early Recognition and Management.Journal of clinical medicine · 2023Review
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5 authors.
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Abstract
backgroundNecrotizing 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.
methodsThis 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.
resultsRadiomics 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.
conclusionsNon-contrast-enhanced CT-based radiomics models may be helpful for recognizing NP in early stage.
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