Survival Prediction from Public Brain Metastasis MRI Cohorts: A Positive-Control and Permutation-Based Feasibility Evaluation

Public brain metastasis MRI cohorts are widely used for prognostic modeling, but whether an endpoint is identifiable at the available sample size is rarely tested. We evaluated eight clinical endpoints in two public cohorts, Yale/BraTS-MET (199 patients, 74 deaths) and OpenBTAI/MOLAB (75 patients, all deceased), using lesion-centered nnU-Net encoder embeddings and PyRadiomics features with patient-grouped cross-validation and label permutation. Patient-level infratentorial involvement served as a positive control (balanced accuracy 0.60); the Graded Prognostic Assessment (GPA) score was recoverable from OpenBTAI radiomics (ρ = 0.42), whereas brain age was not recovered in either cohort. Primary tumor type was the only endpoint significant after multiplicity correction (1.3× chance). Survival endpoints were null for clinical models and the planned graph model (C = 0.50, p = 0.49); an exploratory linear model on embeddings reached C ≈ 0.58 (p = 0.07, uncorrected), which PyRadiomics features did not reproduce. No progression definition remained significant after family-wise correction. In simulation, 80% power required a true C-index of about 0.68–0.76, so null survival results cannot exclude moderate effects. GPA did not stratify survival (p = 0.18) in a cohort containing only deceased patients. An extraction error found during revision was corrected.