<ul><li>Researchers present a mathematical model for predicting the growth of glioblastoma (GBL) and identifying patient-specific parameters from neuroimaging data.</li><li>The model utilizes a diffuse-interface mathematical model and a reduced-order modeling strategy trained on synthetic data derived from patient-specific brain anatomies reconstructed from imaging.</li><li>A neural network surrogate learns the inverse mapping from tumor evolution to model parameters, resulting in significant computational speed-up while maintaining high accuracy.</li><li>The study establishes a foundation for the development of patient-specific digital twins in neuro-oncology for future clinical applications.</li></ul>

Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks

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