Inflammatory Markers and Advanced Lung Cancer Inflammation Index Predict Recurrence Patterns in Endometrial Cancer: A Retrospective Cohort Study

BACKGROUND: Systemic inflammatory and host-related markers have demonstrated prognostic utility across multiple malignancies; however, their specific role in predicting recurrence patterns in endometrial cancer remains inadequately characterized. Notably, the Advanced Lung Cancer Inflammation Index (ALI), a novel host-related biomarker, has not yet been evaluated in this context. METHODS: We retrospectively analyzed 687 patients with endometrial cancer treated between 2010 and 2020. Pretreatment inflammatory markers, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), lymphocyte-to-monocyte ratio (LMR), neutrophil percentage-to-albumin ratio (NPAR), and ALI, were calculated based on routine laboratory data. Receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values for these markers. Multivariate logistic regression and multinomial regression were employed to assess predictors of recurrence and recurrence patterns, respectively. The C-index was used to evaluate the discriminative ability of the prediction models. RESULTS: Over a median 52-month follow-up, 127 patients (18.5%) recurred. Optimal cut-offs: NLR > 2.45, PLR > 158, LMR ≤ 3.85, NPAR > 11.2, ALI ≤ 45.6. Multivariate analysis identified NLR (OR = 2.18), PLR (OR = 1.67), LMR (OR = 1.75), and ALI (OR = 2.42) as independent predictors (all p < 0.008). NLR was more predictive in early-stage disease (interaction p = 0.032). Distinct patterns emerged: NLR predicted distant recurrence (RRR = 3.12), PLR was associated with local recurrence (RRR = 2.03), and ALI was associated with recurrence across all sites (local RRR = 1.82; regional RRR = 2.21; distant RRR = 3.45). The host-related biomarker model demonstrated superior discrimination (C-index = 0.821) versus FIGO staging alone (0.718, p < 0.001). CONCLUSIONS: Pre-treatment inflammatory markers independently predict endometrial cancer recurrence with distinct site-specific patterns. ALI is a promising composite host-related biomarker that significantly improves risk stratification, offering a foundation for the design of prospective studies of personalized surveillance strategies.