Prognostic value of pretreatment systemic immune-inflammation index towards tumor response in patients with locally advanced cervical cancer treated with definitive radiotherapy in Indonesia.

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Background: The systemic immune-inflammation (SII) is a parameter reflecting immune and inflammatory response. However, the optimal SII cut-off values vary across populations and disease entities. This study aims to evaluate the prognostic value of SII towards tumor response among patients with locally advanced cervical cancer (LACC) in Indonesia. Methods: We conducted a retrospective study of patients with LACC who underwent definitive (chemo)radiotherapy at Dr. Cipto Mangunkusumo Hospital, Indonesia, in 2019 - 2021. Clinical, tumor-related, laboratory, and treatment-related data were collected from medical records. Tumor response was assessed using radiologic imaging following completion of radiotherapy, divided into complete and non-complete response. The SII was calculated based on platelet count and neutrophil-lymphocyte ratio. The optimal SII cut-off value was determined using ROC curve analysis. Factors associated with tumor response were evaluated using bivariate and multivariate analyses. Results: This study included 130 eligible patients, with a mean age of 51 years. Most patients were diagnosed with stage III (83.1%), squamous cell carcinoma (82.3%), and nodal involvement (73.8%). Complete response was achieved in 66.9% of patients following radiotherapy, with a median total dose of 80 Gy. The optimal SII cut-off value for this population was 1900 x 10 ³ cell/µL (AUC: 0.643; p = 0.008). Based on bivariate and multivariate analysis, a low baseline SII (hazard ratio [HR]: 2.87; p = 0.031) and administering concurrent chemoradiotherapy (CCRT) (HR: 3.22; p = 0.007) were the only factors independently associated with better tumor response. Conclusions: Pre-treatment SII is a reliable predictor of tumor response in LACC patients, with improved responses observed in patients receiving concurrent chemoradiotherapy.

Bivariate and multivariate analysis for tumor response.