Abstract
Background
Long-term survival after oesophagectomy remains poor, with recurrence a feared common outcome. Prediction tools can help clinicians identify high-risk patients and optimise treatment decisions based on their prognostic factors. This study developed and evaluated a prediction model to predict long-term survival and time-to-recurrence following surgery for oesophageal cancer.
Methods
Patients who underwent curative surgery between June 2009-2015 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 in the final model. 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% and moderate agreement in the <20% and >80% quintile groups.
Conclusions
This study demonstrated the ability of a statistical model to accurately predict long-term survival and time-to-recurrence after surgery for oesophageal cancer, with CPH and RSF models showing good discrimination and calibration. Identification of patient groups at risk of recurrence and poor long-term survival can improve patient outcomes by enhancing selection of treatment methods and surveillance strategies. Future work evaluating prediction-based decisions against standard decision-making is required to improve understanding of the clinical utility derived from prognostic model use.
Long-term survival probabilities for childhood rhabdomyosarcoma
