A multimodal MRI-radiomics deep learning model for survival risk stratification after gamma knife radiosurgery in patients with brain metastases: A multicenter retrospective study.

BACKGROUND: Brain metastasis (BM) is a high-mortality complication occurring in 20-40% of cancer patients. While the Gamma Knife (GK) is a primary treatment, individualized prognostic prediction remains limited. This study develops and validates a multimodal deep-learning framework for overall survival risk stratification after GK. PATIENTS AND METHODS: This multicenter retrospective study includes 875 patients across three centers. A mask-guided multi-scale encoder is applied to extract MRI features. The proposed model integrated full MRI, grid-based MRI patches, radiomics, and consistently available clinical variables to generate a patient-level log-risk score for overall survival. Performance is assessed via time-dependent AUC, C-index, and Decision Curve Analysis (DCA). RESULTS: The model achieves 1-year AUCs of 0.870 (Training), 0.755 (Internal Val), 0.740 (External Val 1), and 0.788 (External Val 2). C-indices remain moderate across validation cohorts (0.655, 0.653, and 0.649). Multivariable Cox regression showed that the model-derived risk score was independently associated with overall survival across all cohorts. Using a training-derived exploratory threshold of 0.17, the model stratified patients into high- and low-risk groups with significant differences in overall survival across all cohorts. DCA suggests the potential net benefit at 12 months. CONCLUSION: The proposed multimodal model showed consistent but moderate discrimination for overall survival stratification in BM patients. By integrating multimodal data, the framework may provide incremental prognostic information for post-GK risk stratification. Further recalibration, incorporation of comprehensive clinical variables, and prospective validation are warranted before clinical implementation.