Nomogram Based on Immune-Inflammatory Score and Classical Clinicopathological Parameters for Predicting the Recurrence of Endometrial Carcinoma: A Large, Multi-Center Retrospective Study

Background: Surgery is the best approach to treat endometrial cancer (EC); however, there is currently a deficiency in effective scoring systems for predicting EC recurrence post-surgical resection. This study aims to develop a clinicopathological-inflammatory parameters-based nomogram to accurately predict the postoperative recurrence-free survival (RFS) rate of EC patients.

Methods: A training set containing 1068 patients and an independent validation set consisting of 537 patients were employed in this retrospective study. The prognostic factors for RFS were identified by univariable and multivariable Cox proportional hazards regression analyses, and integrated into nomogram. The C-index, area under the curves (AUC), and calibration curves were employed to determine the predictive discriminability and accuracy of nomogram. Utilizing the nomogram, patients were stratified into low- and high-risk groups, and the Kaplan-Meier survival curve was further employed to assess the clinical efficacy of the model.

Results: Cox regression analyses revealed that age (HR = 1.769, P = 0.002), FIGO staging (HR = 1.790, P = 0.018), LVSI (HR = 1.654, P = 0.017), Ca125 (HR = 1.532, P = 0.023), myometrial invasion (HR = 1.865, P = 0.001), cervical stromal invasion (HR = 1.655, P = 0.033), histology (HR = 2.637, P < 0.001), p53 expression (HR = 1.706, P = 0.002), PLR (HR = 1.971, P = 0.003), SIRI (HR = 2.187, P = 0.003), and adjuvant treatment (HR = 0.521, P = 0.003) were independent prognostic factors for RFS in patients with EC. A combined clinicopathologic-inflammatory parameters model was constructed, which outperformed the single-indicator model and other established models in predicting the 1-, 3-, and 5-year RFS rates in patients with EC.

Conclusion: The nomogram demonstrated sufficient accuracy in predicting the RFS probabilities of EC, enabling personalized clinical decision-making for future clinical endeavors.