Kang Sokbom, Lee Jong-Min, Lee Jae-Kwan, Kim Jae-Weon, Cho Chi-Heum, Kim Seok-Mo, Park Sang-Yoon, Park Chan-Yong, Kim Ki-Tae
*Center for Uterine Cancer, National Cancer Center, Goyang; †Department of Obstetrics and Gynecology, School of Medicine, Kyung Hee University; ‡Department of Obstetrics and Gynecology, Korea University College of Medicine; §Department of Obstetrics and Gynecology, Cancer Research Institute, College of Medicine, Seoul National University, Seoul; ∥Department of Obstetrics and Gynecology, Dongsan Medical Center, Keimyung University, Daegu; ¶Department of Obstetrics and Gynecology, Chonnam National University, Gwangju; #Department of Obstetrics and Gynecology, Gachon University Hospital, Incheon; and **Department of Obstetrics and Gynecology, Busan Paik Hospital, Inje University, Busan, South Korea.
Int J Gynecol Cancer. 2014 Mar;24(3):513-9. doi: 10.1097/IGC.0000000000000090.
The purpose of this study is to develop a Web-based nomogram for predicting the individualized risk of para-aortic nodal metastasis in incompletely staged patients with endometrial cancer.
From 8 institutions, the medical records of 397 patients who underwent pelvic and para-aortic lymphadenectomy as a surgical staging procedure were retrospectively reviewed. A multivariate logistic regression model was created and internally validated by rigorous bootstrap resampling methods. Finally, the model was transformed into a user-friendly Web-based nomogram (http://http://www.kgog.org/nomogram/empa001.html).
The rate of para-aortic nodal metastasis was 14.4% (57/397 patients). Using a stepwise variable selection, 4 variables including deep myometrial invasion, non-endometrioid subtype, lymphovascular space invasion, and log-transformed CA-125 levels were finally adopted. After 1000 repetitions of bootstrapping, all of these 4 variables retained a significant association with para-aortic nodal metastasis in the multivariate analysis-deep myometrial invasion (P = 0.001), non-endometrioid histologic subtype (P = 0.034), lymphovascular space invasion (P = 0.003), and log-transformed serum CA-125 levels (P = 0.004). The model showed good discrimination (C statistics = 0.87; 95% confidence interval, 0.82-0.92) and accurate calibration (Hosmer-Lemeshow P = 0.74).
This nomogram showed good performance in predicting para-aortic metastasis in patients with endometrial cancer. The tool may be useful in determining the extent of lymphadenectomy after incomplete surgery.
本研究旨在开发一种基于网络的列线图，用于预测子宫内膜癌分期不完全患者腹主动脉旁淋巴结转移的个体化风险。
回顾性分析了来自8家机构的397例行盆腔和腹主动脉旁淋巴结清扫术作为手术分期程序的患者的病历。通过严格的自助重采样方法创建并内部验证了多因素逻辑回归模型。最后，将该模型转化为用户友好的基于网络的列线图（http://http://www.kgog.org/nomogram/empa001.html）。
腹主动脉旁淋巴结转移率为14.4%（57/397例患者）。采用逐步变量选择法，最终纳入了4个变量，包括肌层深层浸润、非子宫内膜样亚型、淋巴管间隙浸润和经对数转换的CA-125水平。经过1000次自助抽样重复，在多因素分析中，所有这4个变量均与腹主动脉旁淋巴结转移显著相关——肌层深层浸润（P = 0.001）、非子宫内膜样组织学亚型（P = 0.034）、淋巴管间隙浸润（P = 0.003）和经对数转换的血清CA-125水平（P = 0.004）。该模型显示出良好的区分度（C统计量 = 0.87；95%置信区间，0.82 - 0.92）和准确的校准度（Hosmer-Lemeshow P = 0.74）。
该列线图在预测子宫内膜癌患者腹主动脉旁转移方面表现良好。该工具可能有助于确定不完全手术后淋巴结清扫的范围。
