Metabolic risk score as a predictor in a nomogram for assessing myometrial invasion for endometrial cancer
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- Published online on: February 6, 2023 https://doi.org/10.3892/ol.2023.13700
- Article Number: 114
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Copyright: © Qiang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
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Abstract
The purpose of the present study was to investigate the predictive value of metabolic syndrome in evaluating myometrial invasion (MI) in patients with endometrial cancer (EC). The study retrospectively included patients with EC who were diagnosed between January 2006 and December 2020 at the Department of Gynecology of Nanjing First Hospital (Nanjing, China). The metabolic risk score (MRS) was calculated using multiple metabolic indicators. Univariate and multivariate logistic regression analyses were performed to determine significant predictive factors for MI. A nomogram was then constructed based on the independent risk factors identified. A calibration curve, a receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were used to evaluate the effectiveness of the nomogram. A total of 549 patients were randomly assigned to a training or validation cohort, with a 2:1 ratio. Data was then gathered on significant predictors of MI in the training cohort, including MRS [odds ratio (OR), 1.06; 95% confidence interval (CI), 1.01‑1.11; P=0.023], histological type (OR, 1.98; 95% CI, 1.11‑3.53; P=0.023), lymph node metastasis (OR, 3.15; 95% CI, 1.61‑6.15; P<0.001) and tumor grade (grade 2: OR, 1.71; 95% CI, 1.23‑2.39; P=0.002; Grade 3: OR, 2.10; 95% CI, 1.53‑2.88; P<0.001). Multivariate analysis indicated that MRS was an independent risk factor for MI in both cohorts. A nomogram was generated to predict a patient's probability of MI based on the four independent risk factors. ROC curve analysis showed that, compared with the clinical model (model 1), the combined model with MRS (model 2) significantly improved the diagnostic accuracy of MI in patients with EC (area under the curve in model 1 vs. model 2: 0.737 vs. 0.828 in the training cohort and 0.713 vs. 0.759 in the validation cohort). Calibration plots showed that the training and validation cohorts were well calibrated. DCA showed that a net benefit is obtained from the application of the nomogram. Overall, the present study developed and validated a MRS‑based nomogram predicting MI in patients with EC preoperatively. The establishment of this model may promote the use of precision medicine and targeted therapy in EC and has the potential to improve the prognosis of patients affected by EC.
