Machine learning models based on chest computed tomography for identifying plastic bronchitis in children with
Yanjie Xu1,2, Zhiling Liu3, Jingshuo Li4
1Department of Radiology, Shandong Provincial Qianfoshan Hospital, Shandong University, Jinan, China.
Insights
Machine learning accurately predicts plastic bronchitis in children with Mycoplasma pneumoniae pneumonia using clinical and CT scan data. This aids early treatment for better outcomes.
Area of Science:
- Pediatric Pulmonology
- Medical Imaging
- Artificial Intelligence in Medicine
Background:
- Plastic bronchitis (PB) is a serious complication of Mycoplasma pneumoniae pneumonia (MPP).
- Early identification of PB in children with MPP is crucial for timely and effective treatment.
- Lung consolidation is a common finding in children with MPP.
Purpose of the Study:
- To develop and validate a machine learning (ML) model for predicting PB in children with MPP and lung consolidation.
- To integrate clinical risk factors and chest computed tomography (CT) features for improved prediction accuracy.
- To establish a reliable tool for early PB detection, potentially avoiding invasive procedures.
Main Methods:
- Retrospective analysis of 777 children with MPP and lung consolidation from three centers.
- Utilized least absolute shrinkage and selection operator regression for radiomics feature selection.
- Developed a multifactorial prediction model combining clinical and radiological data.
- Evaluated model performance using ROC curves and decision curve analysis (DCA).
Main Results:
- A multifactorial model incorporating seven radiomics features and pleural effusion achieved high predictive performance.
- Area under the curve (AUC) values ranged from 0.770 to 0.831 across training, test, and validation sets.
- The multifactorial model significantly outperformed CT-only and clinical-only prediction models.
- Calibration curves and DCA confirmed the model's accuracy and clinical utility.
Conclusions:
- The developed multifactorial ML model enables early and reliable identification of PB in pediatric MPP.
- This predictive tool can assist clinicians in initiating appropriate treatment before invasive diagnostic procedures like fiberoptic bronchoscopy.
- The integration of clinical and CT-derived radiomics features offers a promising approach for managing PB in children.
Background:
Early identification of plastic bronchitis (PB) in Mycoplasma pneumoniae pneumonia (MPP) is very important, as it may help to initiate appropriate treatment early. We aimed to establish a machine learning (ML) model integrating clinical risk factors and chest computed tomography (CT) features to predict PB in children with MPP complicated with lung consolidation.
Methods:
This retrospective study collected 777 children diagnosed with MPP complicated with lung consolidation from three clinical centers between January 2019 and October 2024. Among them, 280 developed PB. The patients were divided into training set [n=373, The Second Qilu Hospital of Shandong University (Center 1)], test set [n=221, Shandong Provincial Hospital Affiliated to Shandong First Medical University (Center 2)], and validation set [n=183, The First Affiliated Hospital of Shandong First Medical University (Center 3)]. The whole lung area on chest CT was defined as region of interest. The least absolute shrinkage and selection operator regression was used to select the most significant radiomics features. Univariate and multivariate logistic regression analyses were conducted to develop a multifactorial model combining radiological and clinical risk factors. Model performance was evaluated using the receiver operating characteristic curve, and clinical usefulness of the models was assessed through decision curve analysis (DCA).
Results:
Seven radiomics features and pleural effusion were used to develop prediction models. The multifactorial model demonstrated the highest area under the curve values of 0.809 [95% confidence interval (CI): 0.763-0.855], 0.770 (95% CI: 0.702-0.839), and 0.831 (95% CI: 0.764-0.897) in the training set, the test set, and the validation set, respectively, which were significantly higher than those of CT-only or clinical-only models. The calibration curves indicated that the multifactorial model achieved superior agreement between predicted and observed outcomes, and DCA showed that it provided a greater net clinical benefit.
Conclusions:
The multifactorial model enabled early, reliable identification of PB in children with MPP prior to fiberoptic bronchoscopy.
