P1.195. A Machine Learning Model for Predicting Anastomotic Leak in Esophageal Cancer Patients Undergoing Esophagectomy: A Single-Center Retrospective Study

Abstract Topic Esophageal Cancer: Surgical Treatment of Esophageal Cancer – early outcomes and complications Background Anastomotic leak (AL) is one of the most severe complications after esophagectomy. Early identification of high-risk patients may help optimize perioperative management and improve outcomes. Machine learning (ML) offers promising tools for clinical prediction, yet studies applying ML to predict AL risk after esophagectomy remain scarce. This study aimed to develop and compare ML-based models using single-center real-world data to identify patients at high risk for AL. Methods Consecutive patients undergoing esophagectomy from April 2019 to April 2023 were retrospectively enrolled. The primary outcome was AL within 30 postoperative days. Thirty-seven potential predictors covering demographics, comorbidities, laboratory values, perioperative factors, and tumor characteristics were collected. Data were split 7:3 into training and test sets. Missing continuous variables were handled by multiple imputation, categorical variables by mode imputation. All features were standardized. Univariate ANOVA (P<0.10) followed by LASSO logistic regression was used for feature selection. Eight ML models were trained and tuned via 5-fold cross-validation with grid search. Performance was evaluated using AUC, accuracy, F1 score, calibration and decision curves. SHAP analysis was performed for interpretability. All analyses were performed in Python with scikit-learn, XGBoost, and SHAP packages. Results 502 patients who underwent esophagectomy were included, with an overall AL rate of 19.3% (97/502). Following data preprocessing and multiple imputation, univariate analysis identified 15 candidate predictors, which were refined to 15 model-level features using LASSO logistic regression. Eight machine learning models were trained. The Stochastic Gradient Boosting Tree (SGBT) achieved the highest AUC in both the training set (0.880) and the test set (0.683, Figure A). Model calibration demonstrated good agreement between predicted and observed outcomes (Figure B). The SGBT model yielded the highest recall (0.456, Figure C), and was therefore selected as the final predictive model. SHAP analysis revealed that radiotherapy, hemoglobin difference, intraoperative bleeding volume, and lymphocyte count were the most influential features driving individual risk predictions (mean absolute SHAP values shown in Figure D; full summary in Figure E). An integrated risk map visualizing the contribution of each key feature is provided in Figure F. Conclusion This single-center study developed ML models for predicting AL after esophagectomy. The SGBT model demonstrated acceptable discriminative ability and identified key risk factors including radiotherapy, hemoglobin drop, bleeding, and lymphocyte count. Despite retrospective limitations, these findings provide a data-driven foundation for risk stratification. Future multicenter validation and prospective studies are warranted to verify clinical utility.