Personalized Predictions of Glioblastoma Infiltration: Mathematical Models, Physics-Informed Neural Networks and
Arxiv
|December 11, 2023
Summary
This study introduces a novel method using Physics-Informed Neural Networks (PINNs) to predict Glioblastoma (GBM) growth from MRI scans. This approach enables faster, personalized treatment planning for brain tumors.
Area of Science:
- Computational biology
- Medical imaging analysis
- Machine learning in oncology
Background:
- Accurate Glioblastoma (GBM) prediction from MRI is vital for treatment.
- Mathematical models aid in understanding tumor spread but require patient-specific parameters.
- Estimating these parameters from limited clinical data is a significant challenge.
Purpose of the Study:
- To develop a method for estimating patient-specific GBM growth parameters from a single MRI scan.
- To integrate mathematical modeling with machine learning for improved prediction accuracy.
- To facilitate personalized radiotherapy treatment planning for Glioblastoma patients.
Main Methods:
- Utilizing Physics-Informed Neural Networks (PINNs) to solve the inverse problem.
- Employing a reaction-diffusion partial differential equation (PDE) model for GBM growth.
- Implementing a two-step parameter estimation: pre-training with non-dimensional parameters and fine-tuning.
- Applying the diffuse domain method to handle complex brain geometry.
Main Results:
- Successful estimation of patient-specific GBM growth parameters from single MRI snapshots.
- Validation of the method on both synthetic and real patient datasets.
- Demonstrated potential for real-time parametric inference in clinical settings.
Conclusions:
- The proposed PINN-based method offers a promising approach for personalized Glioblastoma treatment.
- This technique effectively integrates physical laws with clinical data for enhanced prediction.
- The method shows potential for improving radiotherapy planning and patient outcomes.
