Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans

This repository contains the code the paper Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans

Citation

If you find this code useful in your research, please consider citing:

@misc{zhangPersonalizedPredictionsGlioblastoma2024,
  title = {Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and Multimodal Scans},
  shorttitle = {Personalized Predictions of Glioblastoma Infiltration},
  author = {Zhang, Ray Zirui and Ezhov, Ivan and Balcerak, Michal and Zhu, Andy and Wiestler, Benedikt and Menze, Bjoern and Lowengrub, John},
  year = {2024},
  doi = {10.48550/arXiv.2311.16536},
}

Dataset and Simulations

Dataset and example scripts

Patient data P1-P8 in the paper is obtained from

Lipkova et al., Personalized Radiotherapy Design for Glioblastoma Using Mathematical Tumor Modelling, Multimodal Scans and Bayesian Inference. IEEE Transactions on Medical Imaging (2019) [Paper] [GitHub&Data].