A spatiotemporal state-inference framework for adaptive immunotherapy in glioblastoma

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Immunotherapy has shown limited survival benefits in glioblastoma (GBM) due to modest drug activity and an immunosuppressive microenvironment.
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The GBM Immune–Spatiotemporal Feedback Loop (GBM-ISFL) framework conceptualizes adaptive immunotherapy through longitudinal sensing and phase-matched intervention.
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A Critical Transition Window (CTW) exists during which the tumor–immune ecosystem retains plasticity for therapeutic redirection before resistance hardens.
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Spatiotemporal graph neural networks (STGNNs) are proposed as tools for noninvasive inference of tumor–immune states from multimodal data.
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The GBM-ISFL serves as a hypothesis-generating framework, emphasizing biologic phase over chronology in guiding precision interventions.

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