Early identification of refractory Mycoplasma pneumoniae pneumonia in children using CT-based radiomics: a multicenter study

TL;DR

This study develops a model using clinical, imaging, and radiomics data to predict refractory Mycoplasma pneumoniae pneumonia in children.

Contribution

The novel contribution is an integrated model combining clinical, imaging, and radiomics features for early prediction of refractory Mycoplasma pneumoniae pneumonia.

Findings

The integrated model achieved the highest predictive performance with an AUC of 0.811 in the validation cohort.

Key predictors included D-dimer, fever type, and radiomics features like gray-level co-occurrence matrix and wavelet kurtosis.

Abstract

To develop and validate a model that utilizing clinical, imaging, and radiomics characteristics for early predicting refractory Mycoplasma pneumoniae pneumonia (RMPP) in children.

This multicenter retrospective study included a total of 419 children, divided into training (n = 248), testing (n = 62), and external validation (n = 109) cohorts. Patients were classified into non-RMPP and RMPP groups based on clinical guidelines. Radiomics features were extracted from chest CT scans using PyRadiomics, followed by SelectKBest and least absolute shrinkage and selection operator regression for feature selection. Three random forest-based predictive models were developed: clinical-imaging, radiomics, and integrated. Predictive performance was evaluated using the area under the receiver operating characteristic curve (AUC), McNemar tests, and net reclassification improvement (NRI).

The integrated model demonstrated the highest predictive performance (AUC: 0.811, 95% CI: 0.704–0.917), compared with both the radiomics (AUC: 0.784, 95% CI: 0.683–0.885) and clinical-imaging (AUC: 0.675, 95% CI: 0.603–0.833) models in the validation cohort. McNemar tests revealed significant differences in classification between the radiomics and clinical-imaging models (p = 0.001), radiomics and integrated models (p = 0.013), and clinical-imaging and integrated models (p < 0.001) in the validation cohort. In the validation cohort, the NRI was higher for the integrated model than for the radiomics and clinical-imaging models (both p < 0.001) but did not differ between the radiomics and clinical-imaging models (p = 0.070). Key predictors included D-dimer, type of fever, and the systemic immune-inflammation index, along with radiomics features such as gray-level co-occurrence matrix and wavelet kurtosis.

The integrated model, combining clinical, imaging, and radiomics features, enhances risk stratification for RMPP.

Figures