Correlation between systemic immune inflammatory index and prognostic nutritional index and prognosis of osteosarcoma patients and construction of prediction model.
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BACKGROUND: To explore the predictive value of systemic immune inflammatory index (SII) and prognostic nutritional index (PNI) for the prognosis of patients with osteosarcoma, and to construct and validate an individualized survival prediction model. METHODS: The clinical data of osteosarcoma patients who underwent surgery in the 900th Hospital of the Joint Logistics Support Force from January 2012 to January 2020 were retrospectively analyzed. Preoperative SII (platelets × neutrophils/lymphocytes) and PNI (albumin + 5 × lymphocytes) were calculated, and the optimal cutoff valueswere determined by ROC curve. The Kaplan-Meier method was used to draw survival curves to analyze their relationship with the overall survival time of patients. Pearson correlation analysis was used to analyze the correlation between SII and PNI. Cox regression analysis was used to analyze independent prognostic factors, and the nomogram model was constructed using R software and internally validated. RESULTS: The 5-year overall survival rate of the high SII group was significantly lower than that of the low SII group (27.7% vs. 87.0%, p < 0.05); the 5-year overall survival rate of the low PNI group was significantly lower than that of the high PNI group (23.1% vs. 83.6%, p < 0.05). SII was negatively correlated with PNI (r=-0.410, P < 0.001). The high SII + low PNI group had the worst prognosis, while the low SII + high PNI group had the best prognosis. Cox multivariate analysis showed that SII > 552.60, PNI ≤ 43.38, Enneking stage III, local recurrence, and metastasis were independent risk factors affecting the prognosis of osteosarcoma patients (P < 0.05). A nomogram for predicting the overall survival (OS) of osteosarcoma patients was constructed based on SII, PNI, Enneking stage, local recurrence, and metastasis. The C index of the model was 0.870, and the validation C index was 0.828. The calibration curve showed that the predicted and actual survival rates were highly consistent. The ROC curve shows that the AUC (0.976) of the nomogram prediction model is significantly higher than the indexes such as SII(AUC = 0.811), PNI(AUC = 0.791) and Enneking stage (AUC = 0.761). CONCLUSIONS: SII and PNI are effective biomarkers for the prognosis of osteosarcoma. The nomogram integrating these indices with clinical characteristics better reflects osteosarcoma’s multi-faceted features, providing a stronger basis for individualized treatment.