Xuan Feng, Xing Chen Li, Xiao Yang, Yuan Cheng, Yang Yang Dong, Jing Yuan Wang, Jing Yi Zhou, Jian Liu Wang
BACKGROUND: Lymph node metastasis (LNM) is an important factor affecting endometrial cancer (EC) prognosis. Current controversy exists as to how to accurately assess the risk of lymphatic metastasis. Metabolic syndrome has been considered a risk factor for endometrial cancer, yet its effect on LNM remains elusive. We developed a nomogram integrating metabolic syndrome indicators with other crucial variables to predict lymph node metastasis in endometrial cancer.
METHODS: This study is based on patients diagnosed with EC in Peking University People's Hospital between January 2004 and December 2020. A total of 1076 patients diagnosed with EC and who underwent staging surgery were divided into training and validation cohorts according to the ratio of 2:1. Univariate and multivariate logistic regression analyses were used to determine the significant predictive factors.
RESULTS: The prediction nomogram included MSR, positive peritoneal cytology, lymph vascular space invasion, endometrioid histological type, tumor size > = 2 cm, myometrial invasion > = 50%, cervical stromal invasion, and tumor grade. In the training group, the area under the curve (AUC) of the nomogram and Mayo criteria were 0.85 (95% CI: 0.81-0.90) and 0.77 (95% CI: 0.77-0.83), respectively (P < 0.01). In the validation group (N = 359), the AUC was 0.87 (95% CI: 0.82-0.93) and 0.80 (95% CI: 0.74-0.87) for the nomogram and the Mayo criteria, respectively (P = 0.01). Calibration plots revealed the satisfactory performance of the nomogram. Decision curve analysis showed a positive net benefit of this nomogram, which indicated clinical value.
CONCLUSION: This model may promote risk stratification and individualized treatment, thus improving the prognosis.
