Establishment and verification of a nomogram model based on the inflammatory indicators of patients with cervical cancer for predicting the risk of their lymph node metastasis
Based on inflammatory indicators, this study aimed to predict the risk of lymph node metastasis (LNM) in patients with cervical squamous cell carcinoma (CSCC) and establish a predictive nomogram model.
Methods
This retrospective study analyzed the clinical data of 194 patients with stage IA2-IIA2 who underwent surgery at Hebei General Hospital (between January 2017 and August 2023). Patients were divided into two groups based on the presence of LNM or not. Clinical data of the participants were gathered and analyzed to compare the two groups. Logistic regression analysis was used to analyze the factors influencing LNM in patients with CSCC. R software was used to construct a nomogram model to predict LNM in patients with CSCC, and its accuracy was verified.
Results
Squamous cell carcinoma antigen (SCC-Ag) level, D-dimer level, platelet (PLT) count, and platelet-to-lymphocyte ratio (PLR) index were significantly higher in patients with LNM than in those without LNM (P < 0.05). There was a significant association between lymph vascular space invasion (LVSI) in patients with CSCC and their LNM (P < 0.05). Logistic regression analysis showed that SCC-Ag, PLR, and LVSI in patients with CSCC were independent risk factors for LNM (P < 0.05). A predictive nomogram model was constructed, and the prediction probability was consistent with the actual observed value (Hosmer–Lemeshow P = 0.313). Analyses using the receiver operating characteristic (ROC) curve revealed that the combination of the SCC-Ag, PLR, and LVSI values of patients with CSCC significantly improved the diagnostic efficiency of their LNM (AUC = 0.792, P < 0.001).
Conclusion
Establishing a nomogram model based on preoperative inflammatory indicators of patients with CSCC can accurately predict the risk of LNM, providing evidence for implementing a clinical diagnosis and treatment scheme.