A novel nomogram incorporating preoperative systemic inflammatory response index and clinicopathological parameters
Youlin Deng1, Xiuling Shi1, Chunxia Gong2
1Department of Obstetrics and Gynecology, Chongqing Key Laboratory of Maternal and Fetal Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Objective:
This study aimed to evaluate whether preoperative systemic inflammatory response index (SIRI) could predict lymph node metastasis (LNM) in endometrial cancer (EC), and to develop a nomogram that combines SIRI with clinicopathological parameters for individualized LNM risk assessment.
Methods:
We retrospectively enrolled 1, 336 EC patients who underwent primary surgery. Among them, 947 cases from the First Affiliated Hospital of Chongqing Medical University served as the training cohort, and 389 cases from the Chongqing Maternal and Child Health Hospital comprised the external validation cohort. Preoperative SIRI was calculated using peripheral neutrophil, monocyte, and lymphocyte counts. Logistic regression analyses were used to identify independent predictors of LNM, which were then incorporated into a nomogram. Model performance was evaluated by ROC curves and calibration curves in both cohorts. Kaplan-Meier analysis was applied to compare recurrence-free survival (RFS) and overall survival (OS) between high- and low-risk groups stratified by the model.
Results:
SIRI yielded an area under the curve (AUC) of 0.773 for predicting LNM, with a sensitivity of 74.2% and specificity of 75.8%. The optimal cutoff value of SIRI was 1.115. Multivariate analysis showed that age, CA125, histological type, molecular classification, and SIRI were independently associated with LNM (P < 0.001). A nomogram integrating these five factors was then constructed. This combined model achieved AUC of 0.889 in the training cohort, outperforming the models based solely on SIRI (AUC = 0.750) or solely on clinicopathological parameters (AUC = 0.836). Calibration curves indicated good agreement between predictions and observations. Using a cutoff of 0.136, we divided patients into high- and low-risk groups and found marked differences in both RFS and OS between training and validation cohorts (P < 0.05).
Conclusion:
Preoperative SIRI is an independent predictor of LNM in patients with EC. The nomogram model incorporating SIRI and clinicopathological parameters demonstrated superior performance in predicting LNM compared to models containing either SIRI alone or only traditional parameters, providing a valuable tool for individualized preoperative risk stratification and surgical decision-making.
