Systemic Inflammatory Indices as Accessible Biomarkers for Intracranial Outcome and Prognosis in Driver Gene-Negative NSCLC with Brain Metastases Treated with First-Line Chemoimmunotherapy

Background For patients with driver gene-negative non-small cell lung cancer (NSCLC) with brain metastases, chemoimmunotherapy represents the standard treatment. However, the intracranial objective response rate (iORR) remains limited, highlighting the need for effective predictive biomarkers. This study aimed to evaluate the predictive value of systemic inflammatory indices, including the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and lymphocyte-to-monocyte ratio (LMR). These indices were assessed for their ability to predict intracranial response and survival among these patients. Methods In this retrospective cohort study, 76 patients with driver gene-negative NSCLC and brain metastases who received first-line chemoimmunotherapy were enrolled. Optimal cutoff values of inflammatory indices for predicting iORR were determined using receiver operating characteristic (ROC) curve analysis. Kaplan-Meier survival analysis and Cox regression models were employed to assess associations with progression-free survival (PFS) and overall survival (OS). Results High NLR ( P  = 0.043) and high PLR ( P  = 0.001) were associated with lower iORR, whereas high LMR correlated with higher iORR ( P  = 0.001). In multivariate analysis, LMR was identified as an independent prognostic factor for OS (hazard ratio [HR] = 0.538, P  = 0.032). In the subgroup receiving intracranial radiotherapy, both PLR (HR = 2.519, P  = 0.027) and LMR (HR = 0.499, P  = 0.034) remained independent prognostic factors for OS. Conclusions Systemic inflammatory indices, particularly LMR, serve as reliable and readily accessible biomarkers for predicting intracranial efficacy and long-term survival in driver gene-negative NSCLC patients with brain metastases undergoing chemoimmunotherapy, thereby aiding early risk stratification and individualized treatment decision-making.