Li Xingchen, Cheng Yuan, Dong Yangyang, Zhou Jingyi, Wang Zhiqi, Li Xiaoping, Wang Jianliu
Department of Obstetrics and Gynecology, Peking University People's Hospital, Beijing, China.
Beijing Key Laboratory of Female Pelvic Floor Disorders Diseases, Beijing, China.
Ann Transl Med. 2021 Apr;9(7):538. doi: 10.21037/atm-20-5034.
The purpose of this study was to develop a nomogram that can be used to predict lymph node metastasis (LNM) in patients with endometrial cancer (EC).
The clinical data of EC patients diagnosed between 2004 and 2015 were retrieved from the Surveillance, Epidemiology, and End Results Program (SEER) registry. The nomogram was constructed using independent risk factors chosen by a multivariate logistic regression analysis. Accuracy was validated for both groups using discrimination analysis and calibration curves.
The final study group consisted of 63,836 women that met specific inclusion criteria. The factors that were identified in the multivariate analysis to be significant predictors of LNM were age, tumor size, histological type, myometrial invasion, cervical stromal invasion, and tumor grade in training group (N=42,558). These variables were included in the nomogram. Discriminations of the nomogram and Mayo criteria were 0.848 (95% CI: 0.843-0.853) and 0.806 (95% CI: 0.801-0.812), respectively. In the validation group (N=21,278), the AUC values were 0.847 (95% CI: 0.840-0.857) and 0.804 (95% CI: 0.796-0.813) for the nomogram and the Mayo criteria, respectively (P<0.01). Calibration plots showed that training and validation cohorts were well-calibrated.
A nomogram was developed to predict LNM in EC patients based on a large population-based analysis. The nomogram showed good performance for predicting LNM in patients with EC. This convenient predictive tool may help clinicians to formulate suitable individualized treatment.
本研究旨在开发一种可用于预测子宫内膜癌（EC）患者淋巴结转移（LNM）的列线图。
从监测、流行病学和最终结果计划（SEER）数据库中检索2004年至2015年间诊断为EC患者的临床数据。使用多因素逻辑回归分析选择的独立危险因素构建列线图。通过判别分析和校准曲线对两组的准确性进行验证。
最终研究组由63836名符合特定纳入标准的女性组成。在训练组（N = 42558）中，多因素分析确定的LNM显著预测因素为年龄、肿瘤大小、组织学类型、肌层浸润、宫颈间质浸润和肿瘤分级。这些变量被纳入列线图。列线图和梅奥标准的判别度分别为0.848（95%CI：0.843 - 0.853）和0.806（95%CI：0.801 - 0.812）。在验证组（N = 21278）中，列线图和梅奥标准的AUC值分别为0.847（95%CI：0.840 - 0.857）和0.804（95%CI：0.796 - 0.813）（P < 0.01）。校准图显示训练队列和验证队列校准良好。
基于大规模人群分析开发了一种用于预测EC患者LNM的列线图。该列线图在预测EC患者LNM方面表现良好。这种便捷的预测工具可能有助于临床医生制定合适的个体化治疗方案。
