Development and validation of machine learning models for the prediction of overall survival and cancer-specific survival in endometrial cancer

Abstract Background Accurate prediction of prognosis is essential for the management of patients with cancer. We aimed to predict the prognosis of endometrial cancer using machine learning. Methods We included patients with endometrial cancer in the Surveillance, Epidemiology, and End Results database. We constructed four machine learning models including logistic regression, random forest, gradient boosting machine (XGBoost), and artificial neural network to predict 5-year overall survival (OS) and cancer-specific survival (CSS). The variables included patient demographics (age, race, and year of diagnosis), pathologic factors (clinical stage, histological grade, and TNM classification), and therapeutic factors (surgical content). Results Overall, 71,506 patients for OS and 66,368 patients for CSS were included in the study. For the prediction of OS, XGBoost showed the best performance, with a class accuracy of 0.862 (95%CI: 0.859–0.866) and area under the curve (AUC) of 0.831 (95%CI: 0.827–0.836). Regarding the prediction of CSS, XGBoost also showed the best performance with a class accuracy of 0.914 (95%CI: 0.911–0.916) and AUC of 0.867 (95%CI: 0.862–0.871). Conclusion Using machine learning, we were able to predict the prognosis of endometrial cancer. Future studies should analyze the important variables and suitable algorithms with larger clinical data.