Association between log odds of positive lymph nodes and survival in surgically treated cervical cancer patients: a SEER database–based cohort study

Conventional nodal staging (N stage) and positive lymph node ratio exhibit limitations in accurately evaluating lymph node status. The current research aims to explore the prognostic value of the logarithmic odds of positive lymph nodes (LODDS) for projecting overall survival (OS) and cancer-specific survival (CSS) in patients with cervical cancer (CC) undergoing surgery. Patients who received surgical treatment for CC between 2004 and 2020 were identified from the Surveillance, Epidemiology, and End Results database. Propensity score matching (PSM) was applied to balance baseline characteristics. Hazard ratios (HRs) were analyzed employing Cox proportional hazards regression models. The possible nonlinear relationship was evaluated via restricted cubic splines. Survival rates were compared utilizing Kaplan-Meier curves. Subgroup analyses were also conducted. A novel LODDS-based model was established and verified. Its predictive accuracy was compared with that of the conventional Tumor-Node-Metastasis (TNM) staging model. A total of 9,501 subjects were initially enrolled. Following PSM, 2,092 matched cases were encompassed in the analysis cohort. Multivariable analysis indicated that High LODDS was markedly related to elevated risk of mortality (OS: HR 2.04, 95% confidence interval [CI] 1.747–2.383; CSS: HR 1.821, 95% CI 1.52–2.181; P < 0.001). Model comparison indicated superior predictive performance for the novel LODDS-based model over the conventional model incorporating TNM staging (C-index: 0.743 vs. 0.692). Both the net reclassification improvement and integrated discrimination improvement indices confirmed that the new model offered notably better predictive capability for survival at 1, 3, and 5 years (P < 0.001 for all). LODDS constitutes an independent prognostic factor for individuals with CC following surgery. Compared with conventional TNM staging, LODDS provides incremental prognostic value, facilitating the development of personalized risk assessment and more accurate follow-up strategies.