Metabolic syndrome score as an indicator in a predictive nomogram for lymph node metastasis in endometrial cancer

Abstract

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.

Keywords: Endometrial cancer; Lymph node metastasis; Metabolic; Nomogram.

Fig. 1

Flowchart of study design. 1076 patients were divided into training and validation cohorts according to the ratio of 2:1. Uni-cox and multi-cox regression analyses were conducted to screen significant indicators

Fig. 2

Nomogram of the model and MSR distribution. Endometrial cancer LNM prediction nomogram is depicted (a). For an individual, the values of each variable can be located on the segment representing indicators. A line was drawn upward to determine the point. The sum of the points responds to the likelihood of the LNM. MSR were in normal distribution among patients (b)

Fig. 3

ROC analysis. AUC of Mayo criteria, a model containing three indicators, and the model we constructed in training cohort (a) and validation cohort (b)

Fig. 4

Calibration analyses of the nomogram. Calibration curves predict the overall survival of patients in the training cohort (a) and the validation cohort (b). The x-axis indicates the predicted survival probability, and the y-axis indicates the actual survival probability. The 45-degree line (gray line) indicates that the prediction agrees with actuality

Fig. 5

Decision curve analyses. Net benefit of Mayo criteria and the model in training cohort (a) and validation cohort (b). Model 1, Mayo criteria. Model 2, the nomogram