Nomogram Based on Pan-Immune Inflammation Value and Clinicopathological Parameters for Predicting the Recurrence of Endometrial Cancer and Providing Postoperative Prognostic Management
Evaluation of preoperative pan-immune inflammation value (PIV) combined with clinicopathological parameters in predicting postoperative recurrence of endometrial cancer (EC) and development of a prognostic model for optimized recurrence risk assessment. This retrospective study analyzed a training cohort of 1,275 patients and a validation cohort of 656 patients. Prognostic factors associated with recurrence-free survival (RFS) were identified through univariate and multivariate Cox regression analyses, and a nomogram model was subsequently constructed. The discriminative ability and accuracy of the model were evaluated by using the C-index, area under the curve (AUC), and calibration curve. Patients were stratified into low-risk and high-risk groups based on nomogram, and the clinical utility of the model was validated through Kaplan-Meier survival analysis, providing a robust foundation for clinical decision-making. Cox regression analysis revealed that age (P = 0.012), International Federation of Gynecology and Obstetrics (FIGO) stage (P < 0.001), Ca125 (P = 0.012), lymphovascular space invasion (LVSI) (P = 0.007), myometrial invasion (P < 0.001), histological type (P < 0.001), p53 expression (P = 0.001), adjuvant therapy (P < 0.001), and PIV (P < 0.001) were independent prognostic factors for RFS in EC. We developed a predictive model integrating clinicopathological parameters and PIV, which demonstrated superior performance in predicting 1-, 3-, and 5-year RFS compared with single-indicator models and other conventional models. This nomogram demonstrates high predictive accuracy for RFS in EC patients, offering a robust tool to guide personalized therapeutic strategies in clinical practice.