MRI Biomarkers for Brain Metastases in Lung Cancer Using Machine Learning

Purpose: Existing prognostic models for brain metastases from lung cancer rely on clinical variables and do not capture the biological heterogeneity of host or tumor factors underlying survival outcomes. We evaluated whether MRI-derived radiomic biomarkers improve prognostic stratification beyond clinical variables. We also investigated their biological correlates using matched histopathology. Experimental Design: This retrospective study included 103 patients with brain metastases from lung cancer and matched pre-operative MRI and histopathology. Radiomic features from T1-weighted contrast-enhanced and T2-weighted fluid-attenuated inversion recovery sequences were combined with clinical variables to train Cox proportional hazards, random survival forest, and DeepSurv models for overall survival (OS), CNS progression, and leptomeningeal (LM) progression. Risk groups from an imaging-only model were compared for clinical and histopathologic differences. Results: Imaging features improved prognostic performance beyond clinical variables across all modeling frameworks, with the combined clinical-imaging DeepSurv model achieving the highest concordance index for OS (0.728). Risk groups from the best-performing model for each endpoint differed significantly in OS (median 26.7 vs. 9.1 months, p < 0.001) and LM progression (p = 0.003), but not in CNS progression. Imaging-only risk groups did not differ significantly in any clinical variable, but high-risk patients were less likely to have tumor-infiltrating lymphocytes (46% vs. 77%, p = 0.010) and PD-L1 expression [&ge;] 50% (10% vs. 32%, p = 0.049). Conclusions: MRI radiomic biomarkers provide prognostic information beyond established clinical variables in lung cancer brain metastases and correspond to features of the tumor immune microenvironment, supporting their potential to refine risk stratification.