Yu Zhicheng, Wei Sitian, Zhang Jun, Shi Rui, An Lanfen, Feng Dilu, Wang Hongbo
Department of Obstetrics and Gynecology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, People's Republic of China.
Cancer Manag Res. 2021 Nov 27;13:8879-8886. doi: 10.2147/CMAR.S338861. eCollection 2021.
Existing prognostic models for endometrial cancer are short of facility and effective validation. In this study, we aim to develop and validate a novel prognostic model for endometrial cancer based on clinical characteristics.
The clinical data such as age, BMI (body mass index), FIGO stage, surgical approach, myometrial invasion, grade, lymph node metastasis, pathology and menopause status were collected for constructing and validating the prognostic model from The Cancer Genome Atlas (TCGA) and Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, respectively. COX regression and the least absolute shrinkage and selection operator (LASSO) COX were applied to identify the significant predictors of overall survival (OS) and construct the prognostic model. The discrimination, calibration, and clinical usefulness of the model were evaluated in both cohorts.
Three hundred and sixty-seven and 286 EC patients were collected for training and validation cohort, respectively. A clinical prognostic model integrating six clinical variables including age, BMI, FIGO stage, surgical approach, myometrial invasion and grade was established. K-M analysis shows a significant difference between the low- and high-risk groups. The area under the receiver operating characteristic curve (AUC-ROC) was 0.775 (95% CI, 0.708 to 0.843) and 0.870 (95% CI, 0.758 to 0.982) for the training and validation cohorts which indicating reliable discrimination. The calibration curve revealed excellent predictive accuracy and the Hosmer-Lemeshow test also verified this. Decision curve analysis (DCA) for the prognostic model indicated that it would add more benefits than either the detect-all-patients scheme or the detect-none scheme. In addition, our model has a superior AUC comparing with any single factor as predicting OS.
Our predictive model offers a convenient and accurate tool for clinicians to estimate the prognosis of EC patients.
现有的子宫内膜癌预后模型缺乏便捷有效的验证。在本研究中，我们旨在基于临床特征开发并验证一种新的子宫内膜癌预后模型。
分别从癌症基因组图谱（TCGA）和华中科技大学同济医学院附属协和医院收集年龄、BMI（体重指数）、国际妇产科联盟（FIGO）分期、手术方式、肌层浸润、分级、淋巴结转移、病理及绝经状态等临床数据，用于构建和验证预后模型。应用COX回归和最小绝对收缩与选择算子（LASSO）COX来识别总生存期（OS）的显著预测因子并构建预后模型。在两个队列中评估该模型的辨别力、校准度和临床实用性。
分别收集了367例和286例子宫内膜癌患者作为训练队列和验证队列。建立了一个整合年龄、BMI、FIGO分期、手术方式、肌层浸润和分级这六个临床变量的临床预后模型。K-M分析显示低风险组和高风险组之间存在显著差异。训练队列和验证队列的受试者操作特征曲线下面积（AUC-ROC）分别为0.775（95%CI，0.708至0.843）和0.870（95%CI，0.758至0.982），表明辨别力可靠。校准曲线显示出优异的预测准确性，Hosmer-Lemeshow检验也证实了这一点。预后模型决策曲线分析（DCA）表明，与全部检测方案或全部不检测方案相比，该模型能带来更多益处。此外，与任何单一因素预测OS相比，我们的模型具有更高的AUC。
我们的预测模型为临床医生评估子宫内膜癌患者的预后提供了一种便捷且准确的工具。
