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.

中文翻译：

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目标区域检测算法下子宫内膜癌病变转移的预测模型

在这项工作中，研究了基于目标区域（ROI）目标检测算法的放射学对子宫内膜癌（EC）淋巴结转移的预测价值。选择143例住院的EC患者作为研究对象，随机分为训练组（A组）和测试组（B组）。他们接受了术前盆腔增强磁共振成像（MRI）扫描。应用ROI算法提取特征以构建EC淋巴结放射学模型，并将其与EC淋巴结的综合预测模型进行比较。接收器工作特性（ROC）曲线用于评估放射模型和综合预测模型的诊断效率。结果表明组的曲线（AUC）下，无论是radiomics模型（区域甲 = 0.875，B组的AUC  = 0.882）和综合预测模型（A组的AUC  = 0.917，B组的AUC  = 0.893）具有良好的预测效果，后者的效果明显优于前者。表明ROI目标检测算法的放射学参数是术前预测EC淋巴结转移的有效标志，其全面的预测模型可在临床决策中起到指导作用。