Department of Obstetrics, Huangdao District Hospital of Traditional Chinese Medicine, Qingdao 266500, China.
Department of Gynaecology, Huangdao District Chinese Medicine Hospital, Qingdao 266500, China.
J Healthc Eng. 2021 Aug 26;2021:1148309. doi: 10.1155/2021/1148309. eCollection 2021.
The aim of the study was to investigate the intelligent recognition of radiomics based on the convolutional neural network (CNN) in predicting endometrial cancer (EC). In this study, 158 patients with EC in hospital were selected as the research objects and divided into a training group and a test group. All the patients underwent magnetic resonance imaging (MRI) before surgery. Based on the CNN, the imaging model of EC prediction was constructed according to the characteristics. Besides, the comprehensive prediction model was established through the clinical information and imaging parameters. The results showed that the area under the working characteristic curve (AUC) of the radiomics model and comprehensive prediction model was 0.897 and 0.913 in the training group, respectively. In addition, the AUC of the radiomics model was 0.889 in the test group and that of the comprehensive prediction model was 0.897. The comprehensive prediction model was established through specific imaging parameters and clinical pathological information, and its prediction performance was good, indicating that radiomics parameters could be applied as noninvasive markers to predict EC.
本研究旨在探讨基于卷积神经网络（CNN）的放射组学智能识别在预测子宫内膜癌（EC）中的应用。本研究选取医院收治的 158 例 EC 患者为研究对象，分为训练组和测试组。所有患者均在术前接受磁共振成像（MRI）检查。基于 CNN，根据特征构建 EC 预测的影像模型。此外，通过临床信息和影像参数建立综合预测模型。结果显示，在训练组中，放射组学模型和综合预测模型的工作特征曲线下面积（AUC）分别为 0.897 和 0.913。此外，放射组学模型在测试组中的 AUC 为 0.889，综合预测模型的 AUC 为 0.897。综合预测模型是通过特定的影像参数和临床病理信息建立的，其预测性能良好，表明放射组学参数可作为预测 EC 的无创标志物。
