Overall survival prediction models for gynecological endometrioid adenocarcinoma with squamous differentiation (GE-ASqD) using machine-learning algorithms

Abstract

AbstractThe actual 5-year survival rates for Gynecological Endometrioid Adenocarcinoma with Squamous Differentiation (GE-ASqD) are rarely reported. The purpose of this study was to evaluate how histological subtypes affected long-term survivors of GE-ASqD (> 5 years). We conducted a retrospective analysis of patients diagnosed GE-ASqD from the Surveillance, Epidemiology, and End Results database (2004–2015). In order to conduct the studies, we employed the chi-square test, univariate cox regression, and multivariate cox proportional hazards model. A total of 1131 patients with GE-ASqD were included in the survival study from 2004 to 2015 after applying the inclusion and exclusion criteria and the sample randomly split into a training set and a test set at a ratio of 7:3. Five machine learning algorithms were trained based on nine clinical variables to predict the 5-year overall survival. The AUC of the training group for the LR, Decision Tree, forest, Gbdt, and gbm algorithms were 0.809, 0.336, 0.841, 0.823, and 0.856 respectively. The AUC of the testing group was 0.779, 0.738, 0.753, 0.767 and 0.734, respectively. The calibration curves confirmed good performance of the five machine learning algorithms. Finally, five algorithms were combined to create a machine learning model that forecasts the 5-year overall survival rate of patients with GE-ASqD.