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BIOIMAGING_2026_132.pdf
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Cancer remains a major global health challenge, motivating personalized treatments. PDE-based models can capture tumor dynamics and enable patient-specific predictions, but traditional solvers like FDM or FEM can be computationally costly and require extensive calibration, while purely data-driven neural networks often lack interpretability. Physics-Informed Neural Networks (PINNs) address these limitations by embedding PDE constraints, supporting both forward simulations and inverse parameter estimation. We model glioblastoma growth using a reproducible, open-source PINN framework based on the Fisher–KPP equation. A systematic hyperparameter study evaluates architecture, activation functions, optimizers, learning rates, batching, and sampling strategies. Experiments on synthetic tumors show accurate dynamics and reliable recovery of biophysical parameters. We further provide a standalone Python implementation, transparent datasets, and practical guidelines for reproducible research in personalized oncology.
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Vanderhaeghen, J., Corbet, C., Duhoux, F., Pierreux, C., & Jodogne, S. (2026). Reproducible PINN Framework for Patient-Specific Modeling and Biophysical Parameter Inference in Glioblastoma. Proceedings of the 19th International Joint Conference on Biomedical Engineering Systems and Technologies, 2, 342-349. https://doi.org/10.5220/0014299100004070 (Original work published 2026)