Primary retroperitoneal liposarcoma (PRPLS) is a rare malignancy with a high risk of recurrence and poor prognosis. To evaluate the prognostic value of the systemic inflammation response index (SIRI) in patients with PRPLS undergoing curative-intent surgical resection and to compare its predictive performance with other inflammatory markers. This retrospective study included 122 patients with pathologically confirmed PRPLS who underwent surgical resection at Peking University International Hospital between January 2021 and January 2024. Patients were stratified into high-SIRI and low-SIRI groups based on an optimal cutoff value determined by receiver operating characteristic (ROC) curve analysis. Clinical characteristics, laboratory parameters, and surgical outcomes were compared between groups. Recurrence-free survival (RFS) and locoregional recurrence-free survival (LRFS) were assessed using Kaplan–Meier survival curves and log-rank tests. Univariate and multivariate Cox proportional hazards regression analyses were performed to identify independent prognostic factors. Post-hoc power analysis was conducted to ensure sample adequacy. SIRI demonstrated the highest prognostic accuracy among evaluated inflammatory markers, with an AUC of 0.831 (P < 0.001), outperforming NLR (AUC = 0.724), PLR (AUC = 0.730), and MLR (AUC = 0.747). Patients with high SIRI (≥ 1.95) had significantly shorter RFS and LRFS compared to those with low SIRI (P < 0.001 and P = 0.008, respectively). High SIRI was associated with larger tumor size, higher Ki-67 index, abnormal liver function markers, elevated CRP, and lower albumin levels. In multivariate Cox regression analysis, SIRI remained the only independent predictor of RFS (HR = 5.19, 95% CI: 2.356, P < 0.001). Preoperative SIRI is an independent and superior prognostic biomarker for recurrence in patients with PRPLS following surgical resection. Compared to conventional inflammatory indices, SIRI shows stronger predictive value for both RFS and LRFS, offering a simple and effective tool for risk stratification in clinical practice.
Chen et al. (Fri,) studied this question.