Patient-specific Prediction of Glioblastoma Growth via Reduced Order Modeling and Neural Networks
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Glioblastoma (GBL) is among the most aggressive brain tumors in adults, characterized by patient-specific invasion patterns driven by the underlying brain microstructure. In this work, we present a proof-of-concept for a mechanistic learning framework for patient-specific prediction of GBL growth. The approach integrates a diffuse-interface mathematical model to describe the tumor evolution with machine learning techniques to enable real-time prediction and parameter identification from longitudinal neuroimaging data. A reduced-order modeling strategy, based on proper orthogonal decomposition (POD), is trained on synthetic data generated from patient-specific brain anatomies reconstructed from magnetic resonance imaging (MRI) and diffusion tensor imaging (DTI). A neural network surrogate learns the inverse mapping from tumor evolution to model parameters, achieving a computational speed-up of 99\% while maintaining an accuracy of 96\% in forecasting tumor volume. This hybrid methodology bridges mechanistic modeling and data-driven learning, addressing both the direct and inverse problems of GBL evolution and offering a practical solution for time-sensitive clinical scenarios.
