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Background <p>Lung cancer is the leading cause of cancer-related mortality, with brain metastases (BMs) significantly worsening prognosis. While Immune checkpoint inhibitors (ICIs) have transformed treatment for non-small cell lung cancer (NSCLC), robust prognostic tools are still lacking.</p> Methods <p>The Brain-Lung Immunotherapy Prognostic (BLIP) score was developed using a retrospective cohort of NSCLC patients with BMs treated with ICIs at Karolinska University Hospital, Sweden. Prognostic factors were identified via univariate and multivariable Cox regression. Internal validation employed bootstrap resampling, penalized Cox regression and ROC analysis. External validation was conducted using an independent cohort from Sotiria Thoracic Diseases Hospital of Athens, Greece.</p> Results <p>Of 1844 screened patients, 131 from Karolinska and 109 from Sotiria were included. Key variables were histology, age at BM diagnosis, and number of BMs. The BLIP score stratified patients into “Good” and “Poor” prognosis groups, with median overall survival (OS) of 14.5 and 7 months (hazard ratio [HR]: 0.4; p &lt; 0.0001). External validation confirmed these findings (HR: 0.5; p = 0.0099).</p> Conclusion <p>The BLIP score is a validated prognostic tool for NSCLC patients with BMs receiving ICIs. Incorporating clinical factors, it enhances personalized risk stratification.</p> Highlights <p><UnorderedList Mark="Bullet"> <ItemContent> <p>The BLIP score is a novel prognostic tool for NSCLC with brain metastases undergoing immunotherapy.</p> </ItemContent> <ItemContent> <p>Integrates key clinical factors like histology, age, and metastasis count.</p> </ItemContent> <ItemContent> <p>Internal validation demonstrates strong prognostic power and reliability.</p> </ItemContent> <ItemContent> <p>External validation shows effectiveness across diverse patient populations.</p> </ItemContent> <ItemContent> <p>Stratifies patients into “Good” and “Poor” groups, aiding in personalized treatment decisions.</p> </ItemContent> </UnorderedList></p>

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The brain-lung immunotherapy prognostic (BLIP) Score: a novel robust tool for prognostication in non-small cell lung cancer patients with brain metastases

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月20日

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  • Marcus Skribek,
  • Maria-Effrosyni Livanou,
  • Ioannis Vathiotis,
  • Viktor Strandman,
  • Axel Thorell,
  • Andreas Koulouris,
  • Konstantinos Syrigos,
  • Simon Ekman,
  • Georgios Tsakonas

摘要

Background

Lung cancer is the leading cause of cancer-related mortality, with brain metastases (BMs) significantly worsening prognosis. While Immune checkpoint inhibitors (ICIs) have transformed treatment for non-small cell lung cancer (NSCLC), robust prognostic tools are still lacking.

Methods

The Brain-Lung Immunotherapy Prognostic (BLIP) score was developed using a retrospective cohort of NSCLC patients with BMs treated with ICIs at Karolinska University Hospital, Sweden. Prognostic factors were identified via univariate and multivariable Cox regression. Internal validation employed bootstrap resampling, penalized Cox regression and ROC analysis. External validation was conducted using an independent cohort from Sotiria Thoracic Diseases Hospital of Athens, Greece.

Results

Of 1844 screened patients, 131 from Karolinska and 109 from Sotiria were included. Key variables were histology, age at BM diagnosis, and number of BMs. The BLIP score stratified patients into “Good” and “Poor” prognosis groups, with median overall survival (OS) of 14.5 and 7 months (hazard ratio [HR]: 0.4; p < 0.0001). External validation confirmed these findings (HR: 0.5; p = 0.0099).

Conclusion

The BLIP score is a validated prognostic tool for NSCLC patients with BMs receiving ICIs. Incorporating clinical factors, it enhances personalized risk stratification.

Highlights

The BLIP score is a novel prognostic tool for NSCLC with brain metastases undergoing immunotherapy.

Integrates key clinical factors like histology, age, and metastasis count.

Internal validation demonstrates strong prognostic power and reliability.

External validation shows effectiveness across diverse patient populations.

Stratifies patients into “Good” and “Poor” groups, aiding in personalized treatment decisions.