A Prediction Model of Endometrial Cancer Lesion Metastasis under Region of Interest Target Detection Algorithm

Abstract

The predictive values of region of interest (ROI) target detection algorithm-based radiomics for endometrial cancer (EC) lymph node metastasis was investigate in this work. 143 patients with EC admitted by hospital were selected as the research objects and divided randomly into a training group (group A) and a test group (group B). They received preoperative pelvic-enhanced magnetic resonance imaging (MRI) scanning. The ROI algorithm was applied to extract features to construct an EC lymph node radiomics model that was compared with a comprehensive prediction model of EC lymph node. The receiver operating characteristic (ROC) curve was employed to evaluate the diagnostic efficiency of the radiomic model and comprehensive predictive model. Results showed that both the radiomics model (area under the curve (AUC) of group A = 0.875 and AUC of group B = 0.882) and comprehensive prediction model (AUC of group A = 0.917 and AUC of group B = 0.893) had good predictive effects, and effect of the latter was markedly better than that of the former. It indicated that radiomics parameters of ROI target detection algorithm were effective markers for preoperative prediction of EC lymph node metastasis, and its comprehensive prediction model could play a guiding role in clinical decision-making.