Personalized Predictions of Glioblastoma Infiltration: Mathematical
Models, Physics-Informed Neural Networks and Multimodal Scans
- URL: http://arxiv.org/abs/2311.16536v2
- Date: Tue, 23 Jan 2024 07:01:19 GMT
- Title: Personalized Predictions of Glioblastoma Infiltration: Mathematical
Models, Physics-Informed Neural Networks and Multimodal Scans
- Authors: Ray Zirui Zhang, Ivan Ezhov, Michal Balcerak, Andy Zhu, Benedikt
Wiestler, Bjoern Menze, John Lowengrub
- Abstract summary: Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics.
Mathematical models of GBM growth can complement the data in the prediction of spatial distributions of tumor cells.
This work proposes a method that uses Physics-Informed Neural Networks (PINNs) to estimate patient-specific parameters of a reaction-diffusion PDE model of GBM growth from a single 3D structural MRI snapshot.
- Score: 1.7061727103035216
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is
crucial for understanding tumor growth dynamics and designing personalized
radiotherapy treatment plans.Mathematical models of GBM growth can complement
the data in the prediction of spatial distributions of tumor cells. However,
this requires estimating patient-specific parameters of the model from clinical
data, which is a challenging inverse problem due to limited temporal data and
the limited time between imaging and diagnosis. This work proposes a method
that uses Physics-Informed Neural Networks (PINNs) to estimate patient-specific
parameters of a reaction-diffusion PDE model of GBM growth from a single 3D
structural MRI snapshot. PINNs embed both the data and the PDE into a loss
function, thus integrating theory and data. Key innovations include the
identification and estimation of characteristic non-dimensional parameters, a
pre-training step that utilizes the non-dimensional parameters and a
fine-tuning step to determine the patient specific parameters. Additionally,
the diffuse domain method is employed to handle the complex brain geometry
within the PINN framework. Our method is validated both on synthetic and
patient datasets, and shows promise for real-time parametric inference in the
clinical setting for personalized GBM treatment.
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