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CT-based radiomics models for predicting the prognosis of children with mycoplasma pneumonia
Tianda Wang1, Huan Meng1, LiYong Zhuo1
1Department of Radiology, Hebei Key Laboratory of Precise Imaging of Inflammation Related Tumors, The Affiliated Hospital of Hebei University, Baoding, China.
Insights
A new prognostic model combining clinical data and radiomics features from CT scans accurately predicts outcomes for children with Mycoplasma Pneumonia (MP). This approach enhances diagnostic efficiency for pediatric MP prognosis.
Area of Science:
- Pediatric Pulmonology
- Medical Imaging Analysis
- Computational Pathology
Background:
- Increasing incidence of Mycoplasma Pneumonia (MP) in children necessitates improved prognostic assessment.
- Limited studies have focused on predicting the prognosis of MP specifically in pediatric populations.
- Chest computed tomography (CT) imaging offers potential for quantitative prognostic evaluation.
Purpose of the Study:
- To develop and validate a prognostic model for children diagnosed with Mycoplasma Pneumonia.
- To integrate clinical data with radiomics features from CT scans for enhanced prediction.
- To assess the predictive performance of clinical factors versus radiomics features.
Main Methods:
- A cohort of 356 children with MP was divided into training, validation, and test sets.
- Clinical data (demographics, lab results, CT findings) and radiomics features from CT lesions were collected.
- Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for feature selection and model building.
Main Results:
- Clinical factors (e.g., C-reactive protein, lobe involvement) showed significant prognostic value (AUCs 0.685–0.767).
- A radiomics model achieved higher predictive performance (AUCs 0.701–0.829).
- The combined clinical and radiomics model demonstrated the best prediction accuracy (AUCs 0.748–0.842).
Conclusions:
- A radiomics model utilizing CT imaging features shows promise for quantitatively predicting pediatric MP prognosis.
- Integrating radiomics features with clinical data significantly improves the diagnostic efficiency of prognostic models.
- The combined approach of clinical data and radiomic features offers an effective strategy for predicting outcomes in 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.