Automated machine learning model for predicting anastomotic strictures after esophageal cancer surgery: a retrospective cohort study
- 02.05.2025
Erschienen in:
- Verfasst von
- Junxi Hu
- Qingwen Liu
- Wenbo He
- Jun Wu
- Dong Zhang
- Chao Sun
- Shichun Lu
- Xiaolin Wang
- Yusheng Shu
- Erschienen in
- Surgical Endoscopy | Ausgabe 6/2025
Abstract
Background
Anastomotic strictures (AS) frequently occurs in patients following esophageal cancer surgery, significantly affecting their long-term quality of life. This study aims to develop a machine learning model to predict high-risk AS, enabling early intervention and precise management.
Methods
A total of 1549 patients underwent radical esophageal cancer surgery and were split into a training set (1084) and a validation set (465). Adaptive Synthetic Sampling (ADASYN) handled class imbalance, while Boruta and Least Absolute Shrinkage and Selection Operator (LASSO) with cross-validation refined key features. High-correlation features (r > 0.8) were assessed using variance inflation factors (VIFs) and clinical relevance. Machine learning models were trained and evaluated using area under curve (AUC), accuracy, sensitivity, specificity, calibration curves, and decision curve analysis (DCA). Shapley Additive exPlanations (SHAP) analysis improved model interpretability.
Results
Seven critical variables were finalized, including anastomotic leakage (AL), neoadjuvant therapy (NCRT), suture method (SM), endoscopic assistance (EA), white blood cell count (WBC), albumin (Alb), and Suture site (SS). The Gradient Boosting Machine (GBM) model achieved the highest AUC, with 0.886 in the training set and 0.872 in the validation set. Shapley Additive Explanations (SHAP) analysis indicated that AL, SM, and NCRT were the most significant variables for model predictions.
Conclusion
The GBM machine learning model constructed in this study can effectively identify high-risk patients for AS following esophageal cancer surgery, offering strong support for earlier postoperative detection and precise clinical management.
Graphical abstract
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- Titel
- Automated machine learning model for predicting anastomotic strictures after esophageal cancer surgery: a retrospective cohort study
- Verfasst von
-
Junxi Hu
Qingwen Liu
Wenbo He
Jun Wu
Dong Zhang
Chao Sun
Shichun Lu
Xiaolin Wang
Yusheng Shu
- Publikationsdatum
- 02.05.2025
- Verlag
- Springer US
- Erschienen in
-
Surgical Endoscopy / Ausgabe 6/2025
Print ISSN: 0930-2794
Elektronische ISSN: 1432-2218 - DOI
- https://doi.org/10.1007/s00464-025-11759-5
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