BACKGROUND

Predicting the efficacy of immune-based therapy in patients with unresectable hepatocellular carcinoma (HCC) remains a clinical challenge. This study aims to evaluate the prognostic value of the systemic immune-inflammation index (SII) in forecasting treatment response and survival outcomes for HCC patients undergoing immune-based therapy.

METHODS

We analyzed a cohort of 268 HCC patients treated with immune-based therapy from January 2019 to March 2023. A training cohort of 93 patients received atezolizumab plus bevacizumab (T + A), while a validation cohort of 175 patients underwent treatment with tyrosine kinase inhibitors (TKIs) combined with anti-PD-(L)1 therapy. The SII cutoff value, determined using X-tile analysis based on overall survival (OS) in the training cohort, divided patients into high (> 75210) and low (≤ 75210) SII groups. Prognostic factors were identified through univariate and multivariate logistic and Cox regression analyses, and survival outcomes were assessed using Kaplan-Meier methods. The predictive accuracy of SII was evaluated using receiver operating characteristic (ROC) curves.

RESULTS

An optimal SII cutoff of 752*10 stratified patients into high and low SII groups. Univariate and multivariate logistic regression indicated that SII was a significant predictor of the objective response rate (ORR), which was markedly different between the low and high SII subgroups (34.72% vs. 9.52%, P = 0.019). This finding was consistent in the validation cohort (34.09% vs. 16.28%, P = 0.026). SII also demonstrated prognostic value in Cox regression and Kaplan-Meier analyses. ROC curves confirmed that SII had superior predictive accuracy compared to common clinical indicators, with predictive relevance even in AFP-negative patients. Furthermore, a lower SII was associated with a higher T cell ratio and an increased number of CD8 T cells and Granzyme B CD8 T cells in peripheral blood.

CONCLUSION

SII is a promising predictor of both therapeutic efficacy and prognosis in HCC patients undergoing immune-based treatments. Its application may enhance clinical decision-making, thereby improving patient outcomes from immune-based therapy.