# Development of a radiomics-based model using computed tomography imaging to assess the incidence of extrapulmonary organ involvement in Mycoplasma pneumoniae pneumonia and to predict recovery times: a multicenter study

## TL;DR

This study developed a radiomics-based model to predict extrapulmonary organ involvement and recovery times in children with Mycoplasma pneumoniae pneumonia, showing better accuracy than traditional methods.

## Contribution

The novel contribution is a radiomics-based predictive model that outperforms clinical and imaging-only models for MPP assessment.

## Key findings

- The Radiomics Model outperformed the Clinical Laboratory and Image Feature Models in predicting extrapulmonary organ involvement.
- The Integrated Model achieved the highest predictive performance with an AUC of 0.94 for organ involvement assessment.
- The recovery duration prediction model showed acceptable performance with an R2 score of 0.6825.

## Abstract

This study aimed to develop a predictive model, based on radiomics, to assess the occurrence of extrapulmonary organ involvement and predict recovery durations in children with Mycoplasma pneumoniae pneumonia (MPP).

We retrospectively included 556 confirmed MPP patients from three medical centers between October 2022 and December 2024. Feature parameters were selected and weighted using Z-score normalization and LASSO. A logistic regression model was constructed to assess extrapulmonary organ involvement. Model performance was evaluated using the area under the curve (AUC), calibration curves, and decision curves, with comparisons between models conducted using the DeLong test. For predicting recovery duration, a separate model was developed based on selected features and was evaluated using mean squared error (MSE), the coefficient of determination (R2), and mean absolute error (MAE).

In the evaluation of the extrapulmonary organ involvement model, the Radiomics Model showed statistically significant differences when compared with both the Clinical Laboratory Model [(AUC = 0.73; 95% CI, 0.53–0.88) vs. (AUC = 0.67; 95% CI, 0.49–0.84), p < 0.05] and the Image Feature Model [(AUC = 0.73; 95% CI, 0.53–0.88) vs. (AUC = 0.65; 95% CI, 0.45–0.80), p < 0.05]. Significant differences were observed between the Clinical Laboratory Model and the Image Feature Model in the combined organ involvement group (p < 0.05), but no statistical difference was found in other groups (p > 0.05). The Integrated Model outperformed the Radiomics Model, Clinical Laboratory Model, and Image Feature Model, achieving the highest predictive performance (AUC = 0.94; 95% CI, 0.84–0.99), with all differences being statistically significant (p < 0.01). For predicting recovery duration of extrapulmonary organ involvement, the modified MSE was 6.0, the modified MAE was 1.9, and the modified R2 Score was 0.6825, indicating acceptable prediction performance.

This study demonstrated that incorporating radiomics significantly improved the predictive accuracy of clinical laboratory parameters and imaging features for assessing extrapulmonary organ involvement and forecasting recovery durations in MPP patients. This approach provided an effective tool to enhance diagnostic efficiency for clinicians.