An MRI-informed poromechanical model for organ-scale prediction of glioma growth

TL;DR

This study introduces an MRI-informed poroelastic model for predicting glioma growth in rats, integrating multiple MRI modalities to improve biological accuracy and forecast tumor progression.

Contribution

It presents a novel, MRI-based poromechanical model that captures tumor growth dynamics and tissue mechanics more comprehensively than reaction-diffusion models.

Findings

Tumor volume prediction errors ranged from 4.73% to 36.03%.

Dice scores for segmentation ranged from 0.75 to 0.93.

Model successfully described glioma growth in rat models.

Abstract

Gliomas constitute one of the most aggressive and heterogeneous forms of brain tumors, posing major challenges for understanding their biology and developing effective treatments. Animal models enable the collection of rich longitudinal datasets describing tumor dynamics, which can be integrated within mathematical models to elucidate the biological mechanisms governing tumor growth. While most formulations rely on reaction-diffusion systems with limited insight on tissue deformation and fluid transport, we propose a magnetic resonance imaging (MRI)-informed, poroelastic model to describe C6 glioma growth in rats. We use data from animals (n=4) that were imaged five times after intracranial injection of cancer cells. Each MRI dataset includes (i) anatomical T1-weighted data for brain and tumor segmentation and to assign mechanical properties; (ii) diffusion-weighted MRI, which enables estimation of the fraction of each voxel that is tumor; and (iii) dynamic contrast-enhanced MRI, which informs permeability as well as vascular and liquid fraction maps. Using finite-element simulations, model calibration for each rat uses the Levenberg-Marquardt method informed by the first three MRI datasets. Tumor forecasts are validated by assessing model-data agreement on the remaining two MRI datasets. Our results show relative tumor volume errors between 0.94 percent and 11.27 percent at calibration, and prediction errors between 4.73 percent and 36.03 percent. Additionally, Dice scores ranged from 0.80 to 0.93 during calibration, and from 0.75 to 0.93 during validation. Thus, our results suggest that our poromechanical model can describe C6 glioma growth. This study provides a first step toward a patient-specific, multiscale model of the spatiotemporal poromechanics underlying glioma progression and therapeutic response.