Chen Jingya, Gu Hailei, Fan Weimin, Wang Yaohui, Chen Shuai, Chen Xiao, Wang Zhongqiu
Department of Radiology, Affiliated Hospital of Nanjing University of Chinese Medicine, Jiangsu Province Hospital of Chinese medicine, Nanjing, Jiangsu Province, China.
Department of Radiology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen, Guangdong Province, China.
J Cancer. 2021 Jan 1;12(3):726-734. doi: 10.7150/jca.50872. eCollection 2021.
Preoperative risk stratification is crucial for clinical treatment of endometrial cancer (EC). This study aimed to establish a model based on magnetic resonance imaging (MRI) and clinical factors for risk classification of EC. A total of 102 patients with pathologically proven Stage I EC were included. Preoperative MRI examinations were performed in all the patients. 720 radiomic features were extracted from T2-weighted images. Least absolute shrinkage and selection operator (LASSO) regression model was performed to reduce irrelevant features. Logistic regression was used to build clinical, radiomic and combined predictive models. A nomogram was developed for clinical application. The radiomic model has a better performance than the model based on clinical and conventional MRI characteristics [AUC of 0.946 (95% CI: 0.882-0.973) vs AUC of 0.756 (95% CI: 0.65, 0.86)]. The combined model consisting of radiomic features and tumor size showed the best predictive performance in the training cohort with AUC of 0.955 in the training and 0.889 in the validation cohorts. MRI-based radiomic model has great potential in prediction of low-risk ECs.
术前风险分层对于子宫内膜癌（EC）的临床治疗至关重要。本研究旨在建立一种基于磁共振成像（MRI）和临床因素的EC风险分类模型。共纳入102例经病理证实为I期EC的患者。所有患者均进行了术前MRI检查。从T2加权图像中提取了720个影像组学特征。采用最小绝对收缩和选择算子（LASSO）回归模型减少无关特征。使用逻辑回归建立临床、影像组学和联合预测模型。开发了一种列线图用于临床应用。影像组学模型的性能优于基于临床和传统MRI特征的模型[AUC为0.946（95%CI：0.882 - 0.973），而基于临床和传统MRI特征模型的AUC为0.756（95%CI：0.65，0.86）]。由影像组学特征和肿瘤大小组成的联合模型在训练队列中显示出最佳预测性能，训练队列中的AUC为0.955，验证队列中的AUC为0.889。基于MRI的影像组学模型在预测低风险EC方面具有巨大潜力。
