A predictive model for endometrial cancer recurrence based on molecular markers and clinicopathologic parameters: A double-center retrospective study.

Abstract

Objective: The purpose of this study was to establish a predictive model for endometrial cancer (EC) recurrence based on commonly used molecular markers and clinicopathologic parameters.

Methods: This was a double-center retrospective study. The data of patients were retrospectively collected from two tertiary hospitals in Chongqing, China. The patients were divided into training and validation cohorts according to the ratio of 7:3. In the training cohort, the factors related to the recurrence were screened through uni- and multivariate Cox regression analysis, and a nomogram was constructed based on this. Internal and external validation of the model was performed in two cohorts, respectively. In the training cohort, the optimal risk threshold of the model was determined by using the receiver operating characteristic (ROC) curve and the maximum value of the Youden index.

Results: A total of 1348 patients were included, including 944 in the training cohort and 404 in the validation cohort. Multivariate analysis suggested that ER expression, P53 expression and other clinicopathologic parameters, were significantly related to recurrence. On this basis, a nomogram was constructed to predict 1-, 3-, and 5-year recurrence-free survival (RFS) rate. The model had good predictive accuracy in both cohorts through the validation. The ROC curve and Youden index suggested that the optimal risk threshold of 3-year RFS rate predicted by the model was 0.83, and there was a significant survival difference between the high- and low-risk groups.

Conclusion: Compared to traditional prediction models, the model proposed in this study that combined molecular indicators and clinicopathologic parameters can better predict the prognosis of EC patients.