Systemic immune–inflammation index as a useful prognostic indicator predicts survival in patients with advanced gastric cancer treated with neoadjuvant chemotherapy

Background and objective A novel systemic immune-inflammation index named SII (SII=N×P/L), which is based on neutrophil (N), platelet (P) and lymphocyte (L) counts, has emerged and reflects comprehensively the balance of host inflammatory and immune status. We aimed to evaluate the potential prognostic significance of SII in patients with advanced gastric cancer who received neoadjuvant chemotherapy. Subjects and methods The retrospective analysis included data from 107 patients with advanced gastric cancer undergoing neoadjuvant chemotherapy and 185 patients with pathology-proven gastric cancer. The optimal cutoff value of SII by receiver operating characteristic curve stratified patients into low SII (<600×109/L) and high SII (SII ≥600×109/L) groups. The clinical outcomes of disease-free survival (DFS) and overall survival (OS) were calculated by Kaplan-Meier survival curves and compared using log-rank test. Univariate and multivariate Cox proportional hazards regression models were used to analyze the prognostic value of SII. Results The results indicated that SII had prognostic significance using the cutoff value of 600×109/L on DFS and OS in univariate and multivariate Cox regression survival analyses. Low SII was associated with prolonged DFS and OS, and the mean DFS and OS for patients with low SII were longer than for those with high SII (57.22 vs 41.56 months and 62.25 vs 45.60 months, respectively). Furthermore, we found that patients with low SII had better 1-, 3- and 5-year rates of DFS and OS than those with high SII. In addition, patients with low SII were likely to receive DFS and OS benefits from neoadjuvant chemotherapy and postoperative chemotherapy. Conclusion SII may qualify as a noninvasive, cost-effective, convenient and reproducible prognostic indicator for patients with advanced gastric cancer undergoing neoadjuvant chemotherapy. It may help clinicians to identify those patients who will benefit from treatment strategy decisions.