Construction and validation of prediction model for cervical cancer recurrence based on systemic inflammation response index and clinicopathological parameters

To investigate the predictive value of preoperative systemic inflammatory response index (SIRI) combined with clinicopathological parameters for postoperative recurrence in cervical cancer and to construct a prognostic model in order to optimize recurrence risk assessment.

Patients with cervical cancer who underwent standard surgical treatment at the First Affiliated Hospital of Chongqing Medical University (training cohort, n=996) and Chongqing Maternal and Child Health Hospital (validation cohort, n=496) between January 2017 and January 2022 were retrospectively enrolled based on our strict inclusion and exclusion criteria. Univariate and multivariate Cox regression analyses were performed to identify independent prognostic factors for recurrence-free survival (RFS), and then a nomogram was constructed. Receiver operating characteristic (ROC) curve was plotted to assess the predictive performance of the model, and the area under the curve (AUC) and calibration curve were employed to evaluate the model. Kaplan-Meier survival analysis was performed to determine the clinical application.

Cox regression analysis demonstrated that International Federation of Gynecology and Obstetrics (FIGO) stage (P<0.001), tumor size (P<0.001), pathological type (P<0.001), tumor grade (P=0.007), parametrial invasion (P<0.001), depth of myometrial invasion (P=0.019), lymphovascular space invasion (P=0.019), vaginal margin involvement (P=0.010), adjuvant therapy (P=0.012), and SIRI (P<0.001) were independent prognostic factors for RFS. Our nomogram model based on above prognostic factors exhibited superior predictive performance for 1-, 3-, and 5-year RFS, with a significantly higher AUC value (0.886) than those of single-parameter models.

Our nomogram model demonstrated good accuracy in predicting RFS in cervical cancer patients, providing a potential tool for personalized clinical decision-making in recurrence risk management.