Development and validation of a circulating tumor DNA-based optimization-prediction model for short-term postoperative recurrence of endometrial cancer

Abstract

BACKGROUND

Endometrial cancer (EC) is a common gynecological malignancy that typically requires prompt surgical intervention; however, the advantage of surgical management is limited by the high postoperative recurrence rates and adverse outcomes. Previous studies have highlighted the prognostic potential of circulating tumor DNA (ctDNA) monitoring for minimal residual disease in patients with EC.

AIM

To develop and validate an optimized ctDNA-based model for predicting short-term postoperative EC recurrence.

METHODS

We retrospectively analyzed 294 EC patients treated surgically from 2015-2019 to devise a short-term recurrence prediction model, which was validated on 143 EC patients operated between 2020 and 2021. Prognostic factors were identified using univariate Cox, Lasso, and multivariate Cox regressions. A nomogram was created to predict the 1, 1.5, and 2-year recurrence-free survival (RFS). Model performance was assessed via receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA), leading to a recurrence risk stratification system.

RESULTS

Based on the regression analysis and the nomogram created, patients with postoperative ctDNA-negativity, postoperative carcinoembryonic antigen 125 (CA125) levels of < 19 U/mL, and grade G1 tumors had improved RFS after surgery. The nomogram’s efficacy for recurrence prediction was confirmed through ROC analysis, calibration curves, and DCA methods, highlighting its high accuracy and clinical utility. Furthermore, using the nomogram, the patients were successfully classified into three risk subgroups.

CONCLUSION

The nomogram accurately predicted RFS after EC surgery at 1, 1.5, and 2 years. This model will help clinicians personalize treatments, stratify risks, and enhance clinical outcomes for patients with EC.