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Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and
Hui-Min Mao1, Shun-Gen Huang2, Yang Yang3
1Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Insights
Machine learning models can predict surgical risk in children with pancreaticobiliary maljunction (PBM) and biliary dilatation. The XGBoost model showed the best performance, aiding surgeons in risk assessment.
Area of Science:
- Pediatric Surgery
- Machine Learning in Medicine
- Surgical Risk Assessment
Background:
- Pancreaticobiliary maljunction (PBM) with biliary dilatation poses surgical risks in children.
- Accurate preoperative risk stratification is crucial for surgical planning and patient outcomes.
Purpose of the Study:
- To develop and evaluate machine learning (ML) models for predicting surgical risk in pediatric patients with PBM and biliary dilatation.
- To identify key preoperative factors influencing surgical risk.
Main Methods:
- Development of four ML models: logistic regression (LR), random forest (RF), support vector machine classifier (SVC), and extreme gradient boosting (XGBoost).
- Utilized preoperative data from 157 pediatric patients who underwent surgery for PBM with biliary dilatation.
- Model performance was assessed using the area under the receiver operator characteristic curve (AUC); interpretations were derived from Shapley Additive Explanations (SHAP).
Main Results:
- The XGBoost model achieved the highest AUC (0.822), outperforming LR (0.798), RF (0.802), and SVC (0.804).
- Key predictive features identified across models included choledochal cystic wall enhancement and abnormal right hepatic artery position.
- Other significant factors were choledochal cyst diameter, bile duct variation, and serum amylase levels.
Conclusions:
- Machine learning models, particularly XGBoost, demonstrate significant potential in predicting surgical risk for pediatric PBM and biliary dilatation.
- The developed models and associated nomogram can assist surgeons in early risk identification, potentially preventing intraoperative iatrogenic injuries.
Purpose:
To develop machine learning (ML) models to predict the surgical risk of children with pancreaticobiliary maljunction (PBM) and biliary dilatation.
Methods:
The subjects of this study were 157 pediatric patients who underwent surgery for PBM with biliary dilatation between January, 2015 and August, 2022. Using preoperative data, four ML models were developed, including logistic regression (LR), random forest (RF), support vector machine classifier (SVC), and extreme gradient boosting (XGBoost). The performance of each model was assessed via the area under the receiver operator characteristic curve (AUC). Model interpretations were generated by Shapley Additive Explanations. A nomogram was used to validate the best-performing model.
Results:
Sixty-eight patients (43.3%) were classified as the high-risk surgery group. The XGBoost model (AUC = 0.822) outperformed the LR (AUC = 0.798), RF (AUC = 0.802) and SVC (AUC = 0.804) models. In all four models, enhancement of the choledochal cystic wall and an abnormal position of the right hepatic artery were the two most important features. Moreover, the diameter of the choledochal cyst, bile duct variation, and serum amylase were selected as key predictive factors by all four models.
Conclusions:
Using preoperative data, the ML models, especially XGBoost, have the potential to predict the surgical risk of children with PBM and biliary dilatation. The nomogram may provide surgeons early warning to avoid intraoperative iatrogenic injury.
