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Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and biliary dilatation

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Abstract

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.

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Data availability

The datasets generated and analyzed during the current study are not publicly available due to local restrictions of data protection but are available from the corresponding author on reasonable request.

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Acknowledgements

This work was partially supported by the National Natural Science Foundation of China (No.81971685), Scientific Research Project of Jiangsu Provincial Health Commission (No.ZD2022015) and Science and Technology Development Project of Suzhou(SKY2022054).

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Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China

    Hui-min Mao & Wan-liang Guo

  2. Pediatric Surgery, Children’s Hospital of Soochow University, Suzhou, 215025, China

    Shun-gen Huang

  3. Department of Radiology, Children’s Hospital of Soochow University, Suzhou, 215025, China

    Yang Yang, Tian-na Cai & Wan-liang Guo

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  1. Hui-min Mao
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Correspondence to Wan-liang Guo.

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Ethical statement

The authors are accountable for all aspects of the work to ensure that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki (as revised in 2013). The study protocol was approved by the Ethics Committee of the Children’s Hospital of Soochow University (no. 20150105015).

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Mao, Hm., Huang, Sg., Yang, Y. et al. Using machine learning models to predict the surgical risk of children with pancreaticobiliary maljunction and biliary dilatation. Surg Today 53, 1352–1362 (2023). https://doi.org/10.1007/s00595-023-02696-8

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  • DOI: https://doi.org/10.1007/s00595-023-02696-8

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