Improved preoperative risk stratification in endometrial carcinoma patients: External validation of the ENDORISK Bayesian network model in a large population-based case series

Purpose: Preoperative risk stratification of newly diagnosed endometrial carcinoma (EC) patients has been hindered by only moderate prediction performance for many years. Recently ENDORISK, a Bayesian network model, showed high predictive performance. It was the aim of this study to validate ENDORISK by applying the model to a population-based case series of EC patients. Methods: ENDORISK was applied to a retrospective cohort of women surgically treated for EC from 2003-2013. Prediction accuracy for LNM as well as 5-year DSS was investigated. The model’s overall performance was quantified by the Brier score, discriminative performance by area under the curve (AUC). Results: A complete data set was evaluable from 247 patients. 78.1% cases were endometrioid histotype. The majority of patients (n=156;63.2%) had stage IA disease. Overall, positive lymph nodes were found in 20(8.1%) patients. Using ENDORISK predicted probabilities, most (n=156;63.2%) patients have been assigned to low or very low risk group with a false-negative rate of 0.6%.AUC for LNM prediction was 0.851 (95% confidence interval [CI] 0.761-0.941) with a Brier score of 0.06. For 5-year DSS the AUC was 0.698 (95% CI 0.595-0.800) as Brier score has been calculated 0.09. Conclusions: We were able to successfully validate ENDORISK for prediction of LNM and 5-year DSS. Next steps will now have to focus on ENDORISK performance in daily clinical practice. In Addition, incorporating TCGA-derived molecular subtypes will be of key importance for future extended use. This study may support further promoting of data-based decision-making tools for personalized treatment of EC.