Construction and validation of a Cox regression-based nomogram model for predicting recurrence risk in early endometrial cancer.

OBJECTIVES: To develop and validate a Cox regression-based nomogram model for predicting recurrence risk in early-stage endometrial cancer. METHODS: We retrospectively analyzed 1,540 patients with FIGO stage I-II disease treated between January 2013 and December 2021, of whom 247 (16.04%) experienced recurrence and 1,293 did not. Key predictive factors were identified using Lasso-Cox regression, and a nomogram was constructed and evaluated in training (n=924), validation (n=308), and testing (n=308) cohorts. RESULTS: The model demonstrated strong discriminative ability, with C-index values of 0.748, 0.684, and 0.677, and AUCs of 0.767, 0.701, and 0.694 across the three cohorts. Compared with the traditional Naples Prognostic Score, the nomogram showed significantly better performance in both the training cohort (AUC 0.767 vs. 0.687, P=0.009) and the validation cohort (AUC 0.701 vs. 0.580, P=0.041). Calibration curves showed good agreement between predicted and observed outcomes, and decision curve analysis confirmed substantial net clinical benefit, with net reclassification improvement supporting superior accuracy. CONCLUSIONS: The developed nomogram provides a reliable and effective tool for individualized recurrence risk assessment in early-stage endometrial cancer, demonstrating significant clinical potential for improved risk prediction and treatment planning.