Detecting pancreaticobiliary maljunction in pediatric congenital choledochal malformation patients using machine learning methods.

Abstract

Objective: The presence of pancreaticobiliary maljunction (PBM) in pediatric patients with congenital choledochal malformation significantly impacts clinical management and surgical decision-making. Current preoperative evaluation of PBM coexistence remains challenging in children, while intraoperative cholangiography does not consistently provide diagnostic-quality imaging. This study aims to develop machine learning-based algorithm models for detecting pancreaticobiliary maljunction (PBM) in children with congenital choledochal malformation.

Methods: We conducted a retrospective study utilizing data from patients with congenital choledochal malformation treated at our center between January 2019 and January 2024. Demographic characteristics, clinical features, and preoperative laboratory parameters were processed through rigorous data curation and feature engineering pipelines. Cases were allocated via random sampling into training (80%) and hold-out test (20%) cohorts, maintaining strict separation between training and test cohorts. Seven machine learning algorithms - Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), and K-Nearest Neighbors (KNN) - were implemented with five-fold cross-validation. An ensemble voting classifier was specifically constructed using these models. Model performance was quantified through comprehensive metrics including area under the ROC curve (AUC), sensitivity, specificity, positive/negative predictive values, accuracy, precision, recall, and F1-score. This study employed the nonparametric bootstrap method to estimate the confidence interval for the area under the receiver operating characteristic curve (AUC). SHapley Additive exPlanations (SHAP) was employed for model interpretability, with feature importance rankings determined by absolute SHAP value magnitudes.

Results: In a cohort of 803 pediatric patients with congenital choledochal malformation, 628 (78.2%) demonstrated concurrent pancreaticobiliary maljunction. We developed a detection model incorporating 43 clinical features, with Random Forest showing optimal performance. An ensemble voting classifier integrating seven machine learning algorithms achieved enhanced discriminative performance (AUC: 0.87 (0.81, 0.92); Recall: 0.91 (0.85, 0.95); F1-score: 0.91 (0.87, 0.94)). Key features contributing to PBM detection included: laboratory markers and clinical parameters.

Conclusion: By integrating preoperative clinical symptoms and laboratory parameters, machine learning algorithms demonstrated significant detection capability in identifying PBM among pediatric congenital choledochal malformation patients, with the RF model achieving superior performance metrics among all base models. The developed ensemble voting classifier provides valuable preoperative guidance for surgical planning and clinical management, enabling detection of PBM comorbidity before surgery in congenital choledochal malformation cases.