Deep Learning for Reaction-Diffusion Glioma Growth Modeling: Towards a Fully Personalized Model?

Area of Science:

• Computational biology
• Medical imaging
• Artificial intelligence

Background:

• Reaction-diffusion models are used for glioma growth but face challenges with initialization and parameter estimation for clinical use.
• Personalized prediction of brain tumor progression remains a significant challenge in oncology.

Purpose of the Study:

• To investigate the efficacy of deep convolutional neural networks (DCNNs) in overcoming limitations of reaction-diffusion models for glioma.
• To enable accurate reconstruction of tumor cell density and estimation of model parameters using limited magnetic resonance (MR) data.

Main Methods:

• Trained DCNNs on 1200 synthetic tumors grown on real brain geometries from MR data.
• Validated DCNNs' ability to reconstruct tumor distribution from two imaging contours.
• Assessed DCNNs' capability to estimate diffusivity and proliferation parameters with an additional prior time point contour.

Main Results:

• DCNNs successfully reconstructed whole tumor cell-density distributions from minimal imaging data.
• Accurate estimation of individual tumor model parameters (diffusivity, proliferation) was achieved.
• Demonstrated precise capture of spatio-temporal tumor evolution and applicability to real glioblastoma patient MR data.

Conclusions:

• DCNNs offer a robust solution to initialization and parameter estimation issues in reaction-diffusion glioma models.
• This approach facilitates personalized tumor prognosis and treatment planning by enabling accurate prediction of glioma evolution.
• The study paves the way for clinical integration of advanced computational models in neuro-oncology.