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Development and Internal Validation of a Nomogram for Predicting Recurrence in Endometrial Cancer Based on
Mengdan Miao1, Bingna Huang2, Yirou Jiang2
1Department of Gynecology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, People's Republic of China.
Background:
Endometrial cancer (EC) is a common malignant tumor in the female reproductive system. Identifying patients with a high risk of recurrence is beneficial for formulating personalized follow-up and treatment plans. This study aims to develop a prediction model for evaluating the risk of recurrence of EC after treatment.
Methods:
This study conducted a retrospective analysis on 486 patients with EC and randomly divided them into a training group (n = 389) and a validation group (n = 97). A nomogram was constructed after identifying predictors. The concordance index (C-index), receiver operating characteristic (ROC) curve, calibration plots, net reclassification index (NRI), integrated discrimination improvement (IDI), decision curve analysis (DCA) and Kaplan-Meier curves were used to evaluate the predictive model for EC recurrence.
Results:
A predictive nomogram was constructed based on the six selected predictors. The ROC curve (area under the curve = 0.890) and calibration curve indicate that the model has high discrimination and calibration capabilities. The NRI in the training set was 0.321 (95% CI: 0.031-0.438), and the IDI was 0.133 (95% CI: 0.052-0.215), indicating a significant improvement compared to the ESGO-ESTRO-ESP pattern. The DCA curves indicated that this model exhibited excellent discriminative performance and clinical application value.
Conclusion:
A nomogram based on pathological factors and immunohistochemical indicators was constructed and validated for predicting the recurrence of EC. Its predictive performance was superior to the ESGO-ESTRO-ESP pattern, and it can be used as a prognostic tool for clinical risk stratification.
