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Computed tomography-based radiomic features combined with clinical parameters for predicting post-infectious
Li Zhang1, Ling He1, Guangli Zhang2
1Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, Chongqing, China.
Insights
A new model combining CT radiomic features and clinical data effectively predicts bronchiolitis obliterans (BO) in children with adenovirus pneumonia. This noninvasive approach aids in early detection and management of this serious complication.
Area of Science:
- Pediatric Pulmonology
- Radiology
- Medical Informatics
Background:
- Adenovirus pneumonia is a common respiratory infection in children.
- Bronchiolitis obliterans (BO) is a serious long-term complication of adenovirus pneumonia.
- Accurate prediction of BO is crucial for timely intervention and improved patient outcomes.
Purpose of the Study:
- To develop and validate a predictive model for bronchiolitis obliterans (BO) in children with adenovirus pneumonia.
- To integrate computed tomography (CT) radiomic features with clinical parameters for enhanced predictive accuracy.
- To assess the performance of machine learning models in predicting BO development.
Main Methods:
- Retrospective analysis of 165 children with adenovirus pneumonia, stratified into training (70%) and testing (30%) cohorts.
- Extraction of 2,264 radiomic features from CT lesions, with selection of 10 optimal features.
- Development of combined predictive models using logistic regression, random forest, and support vector machine algorithms, incorporating clinical data (hospitalization length, pneumonia lobes).
Main Results:
- The combined models demonstrated high predictive performance, with Area Under the Curve (AUC) values ranging from 0.859 to 0.977 in the training group and 0.885 to 0.890 in the testing group.
- The models incorporating both radiomic features and clinical parameters significantly outperformed models using only radiomic or clinical data.
- Logistic regression, random forest, and support vector machine models showed strong predictive capabilities, highlighting the utility of integrated data.
Conclusions:
- A combined model utilizing CT-based radiomic features and clinical parameters provides an effective, noninvasive tool for predicting BO in pediatric adenovirus pneumonia.
- This approach can aid clinicians in identifying children at high risk for BO, facilitating early management strategies.
- The study underscores the potential of radiomics in conjunction with clinical data for improving diagnostic and prognostic accuracy in pediatric respiratory diseases.
Objectives:
To develop a model incorporating computed tomography (CT) radiomic features and clinical parameters for predicting bronchiolitis obliterans (BO) with adenovirus pneumonia in children.
Methods:
A total of 165 children with adenovirus pneumonia between October 2013 and February 2020 were enrolled retrospectively. Among them, BO occurred in 70 patients, and the remaining 95 patients did not have BO. These children were stratified into training and testing groups at a ratio of 7:3. Manual segmentation of lesions in baseline CT images during acute pneumonia was performed to extract radiomic features. Multiple statistical methods were used to determine the best radiomic features. Combined models based on radiomic and clinical features were established via logistic regression (LR), random forest (RF), and support vector machine (SVM) algorithms. Model performance was evaluated via the area under the receiver operating characteristic curve (AUC).
Results:
A total of 2,264 radiomic features were extracted from the lesions, from which 10 optimal radiomic features were ultimately selected. The length of hospitalization, number of pneumonia lobes, and optimal radiomic features were incorporated into the combined models. In the training group, the AUCs of the combined LR, RF and SVM models were 0.946, 0.977, and 0.971, respectively; while in the testing group, they yielded AUCs of 0.890, 0.859, and 0.885, respectively. The predictive performance of these combined models surpassed that of the radiomic and clinical models.
Conclusion:
Combining CT-based radiomic features with clinical parameters can offer an effective noninvasive model to predict BO in children with adenovirus pneumonia.
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