Using the Systemic Inflammatory Response Index and the Prognostic Nutritional Index in Predicting Subsequent Bone Metastases in Patients with Non-Small Cell Lung Cancer

Objective: This study aimed to identify the risk factors for Bone Metastases (BM) in patients with non-small cell lung cancer (NSCLC) and develop a scoring system combining the systemic inflammatory response index (SIRI) and prognostic nutritional index (PNI) to predict subsequent bone metastases in patients with NSCLC. Methods: A retrospective analysis of patients with NSCLC treated at our hospital between February 2019 and January 2025 was conducted. Based on the occurrence of BM during follow-up, patients were stratified into the BM or the non-BM group. After their preoperative SIRI and PNI values were calculated, receiver operating characteristic (ROC) curve analysis was used to determine the optimal cut-off values for SIRI and PNI, which were used to establish the SIRI-PNI scoring system. The SIRI-PNI scores were compared between the groups, and univariate and multivariate logistic regression analyses were performed to identify factors influencing BM development. Results: Among the 418 patients included with a mean follow-up of 11.91 ± 0.88 months (range: 9-14 months), 142 were diagnosed with BM (33.97%). ROC analysis determined an optimal threshold of 539.0 for the SIRI (area under the curve [AUC] = 0.877; 95% confidence interval [CI]: 0.807-0.947) and 44.8 for the PNI (AUC = 0.801; 95% CI: 0.689-0.912). An SIRI-PNI scoring system was established, with scores ranging from 0 to 2. Clinical stage, lymph node metastasis, and SIRI-PNI score significantly differed between the BM and non-BM groups (P < 0.05). Multivariate analysis identified clinical stage IV (odds ratio = 11.91, P < 0.0001) and a SIRI-PNI score of 2 (P < 0.0001) as independent risk factors for BM. Conclusion: Advanced clinical stage (IV) and a high SIRI-PNI score (2 points) are significant prognostic indicators for BM development in patients with NSCLC. The preoperative SIRI-PNI scoring system may facilitate early identification of high-risk patients.