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Issue 323: This artwork represents the integration of multimodal imaging, mathematical modeling, and physics-informed machine learning to create personalized digital twins for glioblastoma (GBM) infiltration predictions. On the left, a brain MRI showcases the tumor region, representing real-world medical imaging data. The central schematic illustrates a physics-informed neural network, a machine learning framework that embeds mathematical model to predict tumor growth and infiltration dynamics. The neural network integrates patient-specific data with reaction-diffusion equations, capturing both spatial and temporal dynamics of GBM growth. On the right, a 3D visualization combines predicted tumor regions (yellow and blue) and the brain's anatomy (gray), highlighting the potential of digital twins for personalized predictions. This fusion of physics, machine learning, and multimodal data offers a novel framework for understanding GBM progression, advancing patient-specific treatment strategies. The image emphasizes the synergy between computational methods and clinical data, symbolizing a step toward precision oncology.