Background: Lung cancer remains the leading cause of cancer-related mortality, with brain metastases (BMs) significantly worsening prognosis and quality of life. The advent of immune checkpoint inhibitors (ICIs) has revolutionized the treatment landscape for non-small cell lung cancer (NSCLC). However, precise prognostic tools are essential to optimize clinical decision-making in this context. Methods: The Brain-Lung Immunotherapy Prognostic (BLIP) score was developed based on a retrospective cohort of NSCLC patients treated with ICIs at Karolinska University Hospital, Sweden. Prognostic factors were identified using both univariate and multivariate Cox regression analyses. Internal validation was conducted using bootstrap resampling, penalized Cox regression, k-fold cross-validation, and receiver operating characteristics (ROC) analysis. External validation was performed using an independent cohort from Sotiria Thoracic Diseases Hospital of Athens, Greece. Results: From a total cohort of 1844 patients screened across both study sites, 152 patients from Karolinska University Hospital and 116 from Sotiria Thoracic Diseases Hospital of Athens, Greece, were included in the final analysis. Key prognostic factors influencing outcomes included histology, actionable mutations, age at BM diagnosis, and the number of BMs. The BLIP score effectively stratified patients into two prognostic groups: Good and Poor, with a median overall survival (OS) of 15 and 7 months, respectively (hazard ratio [HR]: 0.4; p < 0.0001). External validation confirmed these findings, showing a significantly lower risk of death for the Good group compared to the Poor group (HR: 0.49; p = 0.0063). The model s robust prognostic performance was confirmed with an area under the ROC curve of 0.87, highlighting its accuracy in predicting survival outcomes. Conclusion: The BLIP score provides a reliable, validated prognostic tool for NSCLC patients with BMs undergoing ICI therapy. By integrating both molecular and clinical variables, it offers significant improvements over existing models. Prospective validation could further support its use in personalized treatment strategies, improving clinical outcomes and patient management.

The authors have declared no competing interest.

M.S. received funding from the Stockholm Cancer Society (grant number: 009618). G.T. was supported by Region Stockholm (clinical postdoctoral appointment). S.E. received funding from the Swedish Cancer Society (CAN2023/29191057), the Stockholm County Council (987911), King Gustaf V Jubilee Fund (204053), and the Sjoberg Foundation (2022-2024). A.K. was supported and funded by research grants from the European Society for Medical Oncology (ESMO) and the Hellenic Society for Medical Oncology (HeSMO). The funders had no role in the collection, the interpretation of data, or the writing of the manuscript. Any views, opinions, findings, conclusions, or recommendations expressed in this material are those solely of the authors and do not necessarily reflect those of the funders.

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The study was approved by the regional ethical review board at Region Stockholm (approval number: 2020-02636). The ethical board waived the need for informed consent.

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