P1.139. Advancing Risk Prediction After Esophagectomy: A Systematic Review of Machine Learning Models

Abstract

Abstract Topic Esophageal Cancer: Other Background Esophagectomy is associated with substantial morbidity, mortality, and resource utilization. Traditional regression-based risk tools may inadequately capture complex nonlinear interactions. Contemporary evidence on machine learning (ML) models predicting postoperative outcomes after esophagectomy was synthesized, focusing on discrimination, validation, and comparison with conventional regression approaches. Methods A PRISMA-guided systematic review was conducted using Embase, MEDLINE, PubMed, and the Cochrane Library in January 2026. A total of 196 studies were identified. After title and abstract screening, 32 studies underwent full-text review, of which 10 met final inclusion criteria as ML-focused prediction models in esophagectomy populations. Extracted data included study design, cohort size, procedure type, predicted outcome, modeling approach (ML versus regression), validation strategy (internal or external), performance metrics (e.g., area under the receiver operating characteristic curve [AUROC]), and reporting elements such as calibration and decision-curve analysis. ML-focused studies were defined as those applying algorithms including gradient boosting, support vector machines, neural networks, or survival forests to postoperative outcome prediction. Results Ten studies applying ML models to esophagectomy outcomes were included (median cohort size 700; range 200–4700). Anastomotic leak was the most frequently predicted outcome (4/10), followed by mortality, major complications, readmission, strictures, and recurrence or survival. Common algorithms included gradient boosting (XGBoost, LightGBM, GBM), support vector machines, neural networks, and survival forests. Reported discrimination ranged from moderate to high (AUROC 0.64 for 90-day mortality and 0.65–0.70 for major complications, increasing to 0.79–0.90 for anastomotic leak prediction; some internally validated models reported AUROC >0.95). Three studies performed independent external validation, and performance generally declined in external cohorts. Comparative analyses demonstrated that ML often matched but did not consistently outperform regression-based models. Conclusion Machine learning models for postoperative risk prediction after esophagectomy demonstrate promising discrimination, particularly for anastomotic leak. Although external validation remains limited, ML approaches are still in early stages of clinical translation. With prospective data integration and robust multicenter validation, ML has the potential to enhance individualized risk stratification, support shared decision-making, guide perioperative planning, and improve allocation of postoperative resources in esophageal surgery.