Predicting the magnitude of risk for non-curative endoscopic submucosal dissection in superficial esophageal cancer using explainable artificial intelligence.
Authors
Affiliations (3)
Affiliations (3)
- Department of Gastroenterology, Affiliated Hospital of North Sichuan Medical College, Nanchong 637000, Sichuan Province, China.
- Department of Gastroenterology, Langzhong People's Hospital, Langzhong 637400, Sichuan Province, China.
- Department of Gastroenterology, Sichuan Branch of National Clinical Research Center for Digestive Diseases, Nanchong 637000, Sichuan Province, China. [email protected].
Abstract
Endoscopic submucosal dissection (ESD) serves as a critical treatment modality for superficial esophageal cancer. However, non-curative resection is significantly associated with residual tumors and unfavorable prognosis. Effective preoperative predictive tools are currently lacking. To develop and validate a machine learning-based prediction model for accurate preoperative assessment of the risk of non-curative ESD resection. This multicenter retrospective study included 366 superficial esophageal cancer patients from the Affiliated Hospital of North Sichuan Medical College as a training set, and 129 patients from Langzhong People's Hospital as an independent external validation set. Predictors were selected using least absolute shrinkage and selection operator and multivariate logistic regression. Nine machine learning classifiers, including logistic regression, LightGBM, and XGBoost, were integrated to develop the models, and SHapley Additive exPlanations (SHAP) were employed to achieve risk visualization. Key predictive factors identified included esophageal stricture, computed tomography-based esophageal wall thickening > 7 mm, endoscopically estimated invasion depth > superficial layer (SM1) (endoscopic ultrasound or magnifying endoscopy with narrow-band imaging collectively referred to as EOM > SM1), multiple lesions, circumferential ratio ≥ 3/4, and preoperative pathological type. The logistic regression model constructed with these factors demonstrated optimal performance (training set area under the curve (AUC) = 0.887; internal validation AUC = 0.872; external validation AUC = 0.849). SHAP analysis further revealed computed tomography-based esophageal wall thickening > 7 mm and EOM > SM1 as core risk-driving factors. The logistic regression prediction model developed in this study effectively identifies patients at high risk of non-curative resection prior to ESD. By incorporating SHAP-based interpretability, the model provides a reliable and transparent tool to support clinical decision-making.