Predicting Long-term Survival and Time-to-recurrence After Esophagectomy in Patients With Esophageal Cancer

Abstract

Objective: To develop prediction models to predict long-term survival and time-to-recurrence following surgery for esophageal cancer. Background: Long-term survival after esophagectomy remains poor, with recurrence common. Prediction tools can identify high-risk patients and optimize treatment decisions based on their prognostic factors. Methods: Patients undergoing curative surgery from the European iNvestigation of SUrveillance After Resection for Esophageal Cancer study were included. Prediction models were developed for overall survival (OS) and disease-free survival (DFS) using Cox proportional hazards (CPH) and random survival forest (RSF). Model performance was evaluated using discrimination [time-dependent area under the curve (tAUC)] and calibration (visual comparison of predicted and observed survival probabilities). Results: This study included 4719 patients with an OS of 47.7% and DFS of 40.9% at 5 years. Sixteen variables were included. CPH and RSF demonstrated good discrimination with a tAUC of 78.2% [95% confidence interval (CI): 77.4%–79.1%] and 77.1% (95% CI: 76.1%–78.1%) for OS and a tAUC of 79.4% (95% CI: 78.5%–80.2%) and 78.6% (95% CI: 77.5%–79.5%), respectively for DFS at 5 years. CPH showed good agreement between predicted and observed probabilities in all quintiles. RSF showed good agreement for patients with survival probabilities between 20% and 80%. Conclusions: This study demonstrated that a statistical model can accurately predict long-term survival and time-to-recurrence after esophagectomy. Identification of patient groups at risk of recurrence and poor long-term survival can improve patient outcomes by optimizing treatment methods and surveillance strategies. Future work evaluating prediction-based decisions against standard decision-making is required to understand the clinical utility derived from prognostic model use.