CT-based radiomics models for predicting the prognosis of children with mycoplasma pneumonia

Background In recent years, the incidence of mycoplasma pneumonia (MP) in children has gradually increased; however, to date, few studies have assessed its prognosis in children. Hence, the present study aimed to develop a prognostic model for children with MP by using clinical data and radiomics features extracted from chest computed tomography (CT) images. Methods A total of 356 children with MP from two hospitals were enrolled in the study. These patients were randomly assigned to a training set (n = 206), a test set (n = 52), and a validation set (n = 98). Clinical data, including patients’ history and demographics, laboratory test results, and CT imaging features, were collected. Radiomics features of the infected lesions were extracted from chest CT images. Univariate analysis was performed to determine the most predictive features with significant differences (P < 0.05) among all imaging and clinical data. Least absolute shrinkage and selection operator was used to select radiomics features and estimate the prediction performance of the clinical factors model and the radiomics model for prognosis. Results In the training set, the clinical factors (creatine kinase-MB, lactate dehydrogenase, C-reactive protein, bilateral lobe pneumonia, number of lobes, and lesion volume) showed a significant prognostic value (all P < 0.05) and were selected to construct the clinical model. The area under the curve (AUC) values of the clinical model for the training, validation, and test sets were 0.767, 0.731, and 0.685, respectively. Twenty one radiomics features were selected. The AUC values of the radiomics model for the training, validation, and test sets were 0.829, 0.775, and 0.701, respectively. Decision curve analysis showed that the radiomics model has potential clinical application value for predicting the prognosis of children with MP. The clinical and radiomics model showed the best prediction performance, with AUC values of 0.842, 0.825, and 0.748 for the training, validation, and test sets, respectively. Conclusion The radiomics model based on CT imaging features showed potential as a quantitative tool to predict the prognosis of children with MP. The addition of radiomics features to the clinical factors improved the diagnostic efficiency of the clinical and radiomics model. Thus, the combination of clinical data and radiomic features effectively predicted the prognostic outcome of children with MP.