An Externally Validated Nomogram for Predicting Lymph Node Metastasis of Presumed Stage I and II Endometrial Cancer - Dong, Yangyang; Cheng, Yuan; Tian, Wenjuan; Zhang, Hua; Wang, Zhiqi; Li, Xiaoping; Shan, Boer; Ren, Yulan; Wei, Lihui; Wang, Huaying; Wang, Jianliu (Frontiers in Oncology, 2019)
Background: Optimal management for endometrial cancer in patients with clinically negative lymph nodes is still under debate. several prediction models for lymphatic dissemination of early-stage endometrial cancer have been developed. However, external validation is rare, and decision curve analysis has hardly been applied for these models. Objective: To develop and validate a nomogram to predict lymph node metastasis of presumed stage I and II endometrial cancer. Study Design: The prediction nomogram was developed by using multivariable logistic regression with data for 700 EC patients who underwent initial surgery from 2006 to 2017 at Peking University People’s Hospital (training dataset), Beijing. Externally validation was performed in 727 eligible patients from Fudan University Shanghai Cancer Center (validation dataset), Shanghai. Results: For the 700 women in the training dataset, the lymph node metastasis rate was 8.0% (56/700). Lymphovascular space invasion, histological grade, cervical stromal invasion and myometrial invasion were independent prognostic factors in the training dataset. We generated a nomogram based on these pathological factors. To determine the clinical usefulness of our nomogram, we compared it with the Mayo criteria. For our nomogram, the area under the receiver operating characteristic curve (AUC) was 0.85 as compared with 0.63 for the Mayo criteria. In the validation dataset, the AUC was 0.78 as compared with 0.57 for the Mayo criteria. The nomogram was well calibrated in both the training and validation datasets. At a 10% probability threshold, our nomogram decreased almost 29 unnecessary lymphadenectomies per 100 patients than the Mayo criteria without missing more metastatic disease. Conclusion: We developed a nomogram to predict lymph node metastasis in patients with early-stage endometrial cancer in China. This prediction model may help clinicians in decision-making for patients with early-stage endometrial cancer, especially for the patient with incomplete surgery, reducing overtreatment and medical costs.