Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
Pediatric Surgery, Children's Hospital of Soochow University, Suzhou, 215025, China.
Surg Today. 2023 Dec;53(12):1352-1362. doi: 10.1007/s00595-023-02696-8. Epub 2023 May 9.
To develop machine learning (ML) models to predict the surgical risk of children with pancreaticobiliary maljunction (PBM) and biliary dilatation.
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.
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.
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.
开发机器学习 (ML) 模型，以预测具有胰胆管合流异常 (PBM) 和胆管扩张的儿童的手术风险。
本研究的对象是 2015 年 1 月至 2022 年 8 月期间因 PBM 伴胆管扩张而行手术的 157 名儿科患者。使用术前数据，开发了四种 ML 模型，包括逻辑回归 (LR)、随机森林 (RF)、支持向量机分类器 (SVC) 和极端梯度提升 (XGBoost)。通过受试者工作特征曲线下面积 (AUC) 评估每个模型的性能。通过 Shapley 加法解释生成模型解释。使用列线图验证性能最佳的模型。
68 名患者（43.3%）被分类为高风险手术组。XGBoost 模型 (AUC=0.822) 优于 LR 模型 (AUC=0.798)、RF 模型 (AUC=0.802) 和 SVC 模型 (AUC=0.804)。在所有四个模型中，胆总管囊肿壁增强和肝右动脉异常位置是两个最重要的特征。此外，胆总管囊肿直径、胆管变异和血清淀粉酶被所有四个模型选为关键预测因素。
使用术前数据，ML 模型，尤其是 XGBoost，具有预测具有 PBM 和胆管扩张的儿童手术风险的潜力。该列线图可向外科医生提供早期预警，以避免术中医源性损伤。
