Patient-specific prediction of glioblastoma growth via reduced order modeling and neural networks
This work addresses the need for personalized treatment planning in neuro-oncology by providing a proof-of-concept for digital twins, though it is incremental as it builds on existing modeling techniques.
The authors tackled the problem of predicting glioblastoma growth by developing a mathematical model that enables real-time, patient-specific predictions from neuroimaging data, achieving significant computational speed-up while preserving high accuracy.
Glioblastoma 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 mathematical model of GBL growth, enabling real-time prediction and patient-specific parameter identification from longitudinal neuroimaging data. The framework exploits a diffuse-interface mathematical model to describe the tumor evolution and a reduced-order modeling strategy, relying on proper orthogonal decomposition, trained on synthetic data derived from patient-specific brain anatomies reconstructed from magnetic resonance imaging and diffusion tensor imaging. A neural network surrogate learns the inverse mapping from tumor evolution to model parameters, achieving significant computational speed-up while preserving high accuracy. To ensure robustness and interpretability, we perform both global and local sensitivity analyses, identifying the key biophysical parameters governing tumor dynamics and assessing the stability of the inverse problem solution. These results establish a methodological foundation for future clinical deployment of patient-specific digital twins in neuro-oncology.