Development and Validation of a Nomogram Based on Metabolic Risk Score for Assessing Lymphovascular Space Invasion in Patients with Endometrial Cancer

A novel predictive model of lymphovascular space invasion in early-stage endometrial cancer

Objective

To predict lymphovascular space invasion (LVSI) positivity in early-stage (stage 1-2) endometrial cancer (EC) using a predictive model with prognostic factors of EC.

Materials and Methods

We included 461 patients who underwent total hysterectomy and bilateral salpingo-oophorectomy with pelvic-paraaortic lymphadenectomy as the primary treatment for presumed early-stage EC at our clinic between 2010 and 2020. Moreover, all surgical specimens were examined histopathologically for the positivity or negativity of LVSI, and the patients were divided into two groups based on these pathologic outcomes. Age, menopausal status, histological type (type 1-2), histological grade (grades 1-2-3), depth of myometrial invasion, and peritoneal cytology results were recorded and analyzed as clinicopathological and demographic characteristics of the patients. The Loess algorithm determined the relationship between the observed and predicted outcomes. The distinction between the algorithms was evaluated by calculating the C-index.

Results

LVSI positivity was significantly associated with advanced age, menopause, type 2 EC, advanced histological grade, malignant peritoneal cytology, cervical involvement, and a tumor exceeding 50% of the myometrial depth (p<0.001, respectively). Remarkably, LVSI was most strongly associated with three explanatory variables: 1- More than 50% myometrial invasion [odds ratio (OR): 3.78; 95% confidence interval (CI): 1.80-7.60], 2- Advanced histological grade [OR=1.98 (1.20-3.20) 95% CI], 3- Malignant peritoneal cytology [OR= 3.06 (1.40-6.30) 95% CI]. The penalized maximum likelihood estimation model correctly classified 87% of the included patients (C-index: 0.876).

Conclusion

Our predictive model may help predict LVSI based on different prognostic factors. The prognostic factors included in the nomogram were significantly associated with LVSI, particularly myometrial invasion depth of more than 50%, advanced histological grade, and malignant peritoneal cytology.

Diagnostic significance and predictive efficiency of metabolic risk score for fertility-sparing treatment in patients with atypical endometrial hyperplasia and early endometrial carcinoma

OBJECTIVE

This study aims to assess the impact of the metabolic risk score (MRS) on both the time to achieve complete remission (CR) of fertility-sparing treatments for atypical endometrial hyperplasia (AEH) and early EC patients.

METHODS

Univariate and multivariate logistic analyses were employed to identify independent risk factors affecting the time to CR with patients at our center. These factors were subsequently incorporated into receiver operator characteristic curve analysis and decision curve analysis to assess the predictive accuracy of time to CR. Additionally, Kaplan-Meier analysis was utilized to determine the cumulative CR rate for patients.

RESULTS

The 173 patients who achieved CR following fertility preservation treatment (FPT) were categorized into three subgroups based on their time to CR (<6, 6-9, >9 months). Body mass index (hazard ratio [HR]=0.20; 95% confidence interval [CI]=0.03, 0.38; p=0.026), MRS (HR=0.31; 95% CI=0.09, 0.52; p=0.005), insulin resistance (HR=1.83; 95% CI=0.05, 3.60; p=0.045), menstruation regularity (HR=3.77; 95% CI=1.91, 5.64; p=0.001), polycystic ovary syndrome (HR=-2.16; 95% CI=-4.03, -0.28; p=0.025), and histological type (HR=0.36; 95% CI=0.10, 0.62; p=0.005) were identified as risk factors for time to CR, with MRS being the independent risk factor (HR=0.29; 95% CI=0.02, 0.56; p=0.021). The inclusion of MRS significantly enhanced the predictive accuracy of time to CR (area under the curve [AUC]=0.789 for Model 1, AUC=0.862 for Model 2, p=0.032). Kaplan-Meier survival curves revealed significant differences in the cumulative CR rate among different risk groups.

CONCLUSION

MRS emerges as a novel evaluation system that substantially enhances the predictive accuracy for the time to achieve CR in AEH and early EC patients seeking fertility preservation.

Development and validation of a prognostic model based on metabolic risk score to predict overall survival of endometrial cancer in Chinese patients

OBJECTIVE

Metabolic syndrome (MetS) is closely related to the increased risk and poor prognosis of endometrial cancer (EC). The purpose of this study was to analyze the relationship between metabolic risk score (MRS) and EC, and establish a predictive model to predict the prognosis of EC.

METHODS

A retrospective study was designed of 834 patients admitted between January 2004 to December 2019. Univariate and multivariate Cox analysis were performed to screen independent prognostic factors for overall survival (OS). A predictive nomogram is built based on independent risk factors for OS. Consistency index (C-index), calibration plots and receiver operating characteristic curve were used to evaluate the predictive accuracy of the nomogram.

RESULTS

The patients were randomly divided into training cohort (n=556) and validation cohort (n=278). The MRS of EC patients, ranging from -8 to 15, was calculated. Univariate and multivariate Cox analysis indicated that age, MRS, FIGO stage, and tumor grade were independent risk factors for OS (p<0.05). The Kaplan-Meier analysis demonstrated that EC patients with low score showed a better prognosis in OS. Then, a nomogram was established and validated based on the above four variables. The C-index of nomogram were 0.819 and 0.829 in the training and validation cohorts, respectively. Patients with high-risk score had a worse OS according to the nomogram.

CONCLUSION

We constructed and validated a prognostic model based on MRS and clinical prognostic factors to predict the OS of EC patients accurately, which may help clinicians personalize prognostic assessments and effective clinical decisions.