To synthesize findings from recent studies on survival prediction in lung cancer patients with brain metastases, focusing on overall survival, progression-free survival, and intracranial progression-free survival.
Approach:
Literature Review: A focused search was conducted across multiple databases for studies published between October 2020 and February 2026, resulting in the selection of fifteen relevant studies.
Key Findings:
Brain metastases occur in approximately 30-40% of lung cancer patients.
Traditional prognostic scoring systems like the Graded Prognostic Assessment (GPA) are widely used, with median overall survival varying by cancer type.
Recent data-driven approaches include radiomics-based models, machine learning survival models, and deep learning frameworks.
Interpretation:
Traditional scoring systems remain clinically useful, but advanced predictive modeling techniques may enhance survival outcome predictions.
Limitations:
Traditional models may not fully capture the complexity of patient outcomes.
The review is limited to studies published in English and may not encompass all relevant research.
Conclusion:
Advanced predictive modeling has the potential to inform personalized treatment plans and improve survival outcomes in lung cancer patients with brain metastases.
