GBM-WAM: Anatomy-guied Latent World-Action Model for Brain Glioblastoma Treatment Planning

Planning next-step treatment for glioblastoma (GBM) requires current magnetic resonance imaging (MRI) and clinical history, while follow-up scans provide longitudinal supervision for modeling treatment-relevant progression beyond direct observation-to-action prediction. World-action models (WAMs) provide a natural paradigm for exploiting such longitudinal information, yet common imagine-thenact designs generate explicit future observations before predicting the action. For volumetric MRI, explicit future generation requires costly reconstruction of dense 3D anatomy and appearance details that are not uniformly relevant to treatment, while treatment-relevant progression is distributed across complementary MRI sequences that may not all be available in clinical practice. We therefore propose GBM-WAM, an anatomy-guided latent WAM that recasts explicit future volume generation as partial-to-complete latent world learning within a causal multimodal large language model (MLLM), enabling training-rich, inference-light planning from variable MRI subsets and clinical context. To learn GBM progression dynamics from partial MRI inputs, a latent world expert combines a mixture-oftransformer architecture with flow matching to predict complete multi-sequence future representations, shaping world query states with shared and sequencespecific progression cues. Concurrently, a temporal mask expert performs statemodulated spatial decoding to supervise localized tumor morphology and longitudinal volumetric changes, providing complementary anatomical guidance. While both auxiliary experts supervise intermediate states during training, they are bypassed at inference to enable direct treatment planning in a single MLLM pass. Experiments on internal and external multicenter cohorts show that GBM-WAM outperforms state-of-the-art methods, achieving up to 93.2% accuracy and up to 91.6% F1 score, while accommodating variable combinations of available MRI sequences and requiring only 0.375 s per case for direct treatment inference.