An MRI-informed poromechanical model for organ-scale prediction of glioma growth
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 T 1 -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 Gauss–Newton 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% and 11.27% at calibration, and prediction errors between 4.73% and 36.03%. 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 towards a patient-specific, multiscale model of the spatiotemporal poromechanics underlying glioma progression and therapeutic response.
Related
- Mechanism-informed dynamic surrogate modeling for rapid thermal-aberration prediction in bonded optical systems
- Physics-Informed Latent Neural Operator for Three-Dimensional Compressor Cascade Flow Prediction
- SteelAgent: LLM-Orchestrated, Physics-Informed Steel Property Prediction with Audited Generalization and Calibrated Uncertainty
- AI-EXAFS: Physics-Informed Prediction of EXAFS from Atomic Structure.
- Toward Reliable Railway-Bogie Response Prediction Using Multifidelity TDNN and Physics-Informed Residual Learning
BibTeX
@article{andaloussi2026mriinformed,
title = {{An MRI-informed poromechanical model for organ-scale prediction of glioma growth}},
author = {M. Abbad Andaloussi and S. Urcun and D.A. Hormuth Ii and G. Lorenzo and G. Sciumè and C. Wu and T.E. Yankeelov and S.P.A. Bordas},
journal = {Computer Methods in Applied Mechanics and Engineering},
year = {2026},
month = oct,
doi = {10.1016/j.cma.2026.119305},
eprint = {2605.05013},
archivePrefix = {arXiv},
url = {https://hal.science/hal-05841185v1},
note = {CC BY-NC-ND},
}
RIS
TY - JOUR
TI - An MRI-informed poromechanical model for organ-scale prediction of glioma growth
AU - M. Abbad Andaloussi
AU - S. Urcun
AU - D.A. Hormuth Ii
AU - G. Lorenzo
AU - G. Sciumè
AU - C. Wu
AU - T.E. Yankeelov
AU - S.P.A. Bordas
PY - 2026
DA - 2026/10/27
JO - Computer Methods in Applied Mechanics and Engineering
DO - 10.1016/j.cma.2026.119305
UR - https://hal.science/hal-05841185v1
AB - 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 T 1 -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 Gauss–Newton 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% and 11.27% at calibration, and prediction errors between 4.73% and 36.03%. 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 towards a patient-specific, multiscale model of the spatiotemporal poromechanics underlying glioma progression and therapeutic response.
SN - 0045-7825
SN - 1879-2138
ER -