Association of systemic immune-inflammation index with all-cause mortality in lung cancer patients undergoing radiotherapy: a retrospective cohort study

BackgroundThe systemic immune-inflammation index (SII) has emerged as a widely studied inflammatory biomarker reflecting the balance between host inflammatory and immune status, although evidence in lung cancer patients undergoing radiotherapy remains limited. This study aimed to investigate the association between baseline SII and all-cause mortality in lung cancer patients undergoing radiotherapy.MethodsA retrospective cohort study was conducted including 489 lung cancer patients who received radiotherapy from January 2022 to January 2025. Baseline SII was calculated as platelet count × neutrophil count/lymphocyte count. Patients were categorized into three tertiles based on SII values: T1 (low SII, ≤480.69, n = 159), T2 (moderate SII, 480.69–857.56, n = 171), and T3 (high SII, >857.56, n = 159). The primary endpoint was all-cause mortality, with follow-up until death or September 2025. Cox proportional hazards models, subgroup analyses, Kaplan–Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and restricted cubic spline (RCS) analysis were employed to assess the relationship between SII and mortality risk.ResultsDuring a median follow-up of 19.9 months, 269 patients (55.0%) died from all causes. Kaplan–Meier analysis showed significantly poorer survival in patients with high SII (T3) compared with those in the low SII group (T1, P < 0.001). In multivariable Cox models, SII remained an independent predictor of all-cause mortality. When analyzed as tertiles, the fully adjusted hazard ratios (HRs) were 2.583 (95% confidence interval [CI]: 1.630–4.095) for T2 and 4.475 (95% CI: 2.841–7.047) for T3 compared with T1 (P for trend < 0.001). Standardized SII also consistently predicted higher mortality risk (HR = 1.222, 95% CI: 1.108–1.347, P < 0.001). Subgroup analyses confirmed stronger associations in patients aged ≤ 60 years and in both sexes. ROC curve analysis demonstrated that SII had strong discriminative power for predicting all-cause mortality, with an area under the curve (AUC) of 0.831 (95% CI: 0.794–0.865, P < 0.001). RCS analysis revealed a significant non-linear relationship between SII and all-cause mortality (P-overall < 0.001; P-nonlinear < 0.001).ConclusionHigh baseline SII predicts higher all-cause mortality in lung cancer patients receiving radiotherapy, indicating its value as a simple and cost-effective prognostic biomarker.