RESUMO
PURPOSE: To establish a nomogram integrating radiomics features based on ultrasound images and clinical parameters for predicting the prognosis of patients with endometrial cancer (EC). MATERIALS AND METHODS: A total of 175 eligible patients with ECs were enrolled in our study between January 2011 and April 2018. They were divided into a training cohort (n = 122) and a validation cohort (n = 53). Least absolute shrinkage and selection operator (LASSO) regression were applied for selection of key features, and a radiomics score (rad-score) was calculated. Patients were stratified into high risk and low-risk groups according to the rad-score. Univariate and multivariable COX regression analysis was used to select independent clinical parameters for disease-free survival (DFS). A combined model based on radiomics features and clinical parameters was ultimately established, and the performance was quantified with respect to discrimination and calibration. RESULTS: Nine features were selected from 1130 features using LASSO regression in the training cohort, which yielded an area under the curve (AUC) of 0.823 and 0.792 to predict DFS in the training and validation cohorts, respectively. Patients with a higher rad-score were significantly associated with worse DFS. The combined nomogram, which was composed of clinically significant variables and radiomics features, showed a calibration and favorable performance for DFS prediction (AUC 0.893 and 0.885 in the training and validation cohorts, respectively). CONCLUSION: The combined nomogram could be used as a tool in predicting DFS and may assist individualized decision making and clinical treatment.