An ultrasound-based radiomics model for survival prediction in patients with endometrial cancer

Background: 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 175 eligible patients with ECs were enrolled in our study between January 2011 and April 2018, which were divided into a training cohort (n = 122) and a validation cohort (n = 53). Least absolute shrinkage and selection operator (LASSO) regression was applied for selection of key features and a radiomics score was calculated. According to the rad-score, patients were stratified into high-risk and low-risk groups. The univariate and multivariable COX regression analysis was used to select independent clinical parameters for disease free survival (DFS). The combined model based on radiomics features and clinical parameters was finally established, and the performance was quantified with respect to discrimination and calibration. Results 9 features were selected from 1130 features using LASSO regression in the training cohort, and yielded the area under the curve (AUC) of 0.823 and 0.792 to predict DFS in training and validation cohorts respectively. Patients with 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 cohort, respectively). Conclusion The combined nomogram could be used as a tool in predicting DFS and may assist individualized decision making and clinical treatment.