36 Predicting Long-Term Survival and Time-to-Recurrence After Oesophagectomy in Patients with Oesophageal Cancer

Aim Long-term survival after oesophagectomy remains poor, with recurrence a feared common outcome. Prediction tools can identify high-risk patients and optimise treatment decisions based on their prognostic factors. This study developed a prediction model to predict long-term survival and time-to-recurrence following surgery for oesophageal cancer. Method 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 48.4% at 5 years. Sixteen variables were included. CPH and RSF demonstrated good discrimination with a tAUC of 78.2% (95% 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 20–80%. Conclusions This study demonstrated that a statistical model can accurately predict long-term survival and time-to-recurrence after oesophagectomy. Identification of patient groups at risk of recurrence and poor long-term survival can improve patient outcomes by optimising 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.