NeuroRAD-FM: A Distributionally Robust Foundation Model for Precision Neuro-Oncology.
Authors
Abstract
Foundation models for neuro-oncology have shown promise for non-invasive molecular characterization and prognostication, yet their clinical utility remains limited by cross-institutional distribution shift and poor performance on under-represented molecular alterations. We developed NeuroRAD-FM, combining self-supervised pretraining, Group-DRO, and a multi-backbone ensemble to learn site-invariant imaging representations. NeuroRAD-FM is pretrained on 7,414 multi-sequence brain MRI examinations aggregated from multiple institutions and evaluated on independent glioma cohorts from UCSF (n = 111), UPenn (n = 95), and an in-house cohort (n = 292). Downstream tasks included automated tumor segmentation, molecular biomarker prediction, and overall survival prediction. Compared with conventional foundation models, NeuroRAD-FM learned more site-invariant features, improved tumor segmentation across tumor types, significantly enhanced molecular marker prediction, and increased concordance indices across all institutions. These findings suggest that integrating Group-DRO with a multi-backbone foundation model enables robust, site-invariant representation learning and substantially improves molecular subtype and survival prediction across diverse neuro-oncology cohorts.