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arxiv: 2605.05013 · v1 · submitted 2026-05-06 · ⚛️ physics.comp-ph

Recognition: unknown

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

Cheguye Wu, David A. Hormuth II, Giuseppe Sciume, Guillermo Lorenzo, Meryem Abbad Andaloussi, Stephane P. A. Bordas, Stephane Urcun, Thomas E. Yankeelov

Authors on Pith no claims yet

Pith reviewed 2026-05-08 15:52 UTC · model grok-4.3

classification ⚛️ physics.comp-ph
keywords poromechanical modelglioma growthMRI-informedporoelasticityfinite elementtumor predictionC6 gliomarat model
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The pith

An MRI-informed poroelastic model predicts C6 glioma growth in rats using serial scans to set mechanical and fluid properties.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper builds a poromechanical model that treats brain tissue as a porous elastic material whose deformation and fluid flow are driven by MRI-derived maps of stiffness, permeability, and fluid content. Serial scans from four rats with implanted C6 gliomas supply the data: T1-weighted images define tumor boundaries and assign properties, diffusion-weighted images estimate tumor cell fraction per voxel, and dynamic contrast-enhanced images yield permeability and vascular fraction fields. The model is calibrated on the first three imaging sessions with a finite-element solver and then tested on the final two sessions. This yields volume errors under 12 percent at calibration and under 37 percent at validation, together with spatial overlap scores above 0.75, showing that the poroelastic description reproduces the observed tumor expansion.

Core claim

Using finite-element simulations of a poroelastic model calibrated with the first three MRI datasets from each of four rats, the authors obtain relative tumor volume errors between 0.94 percent and 11.27 percent during calibration and between 4.73 percent and 36.03 percent during validation on the remaining two datasets, with Dice scores ranging from 0.80 to 0.93 and 0.75 to 0.93 respectively, indicating that the poromechanical model can describe C6 glioma growth.

What carries the argument

The MRI-informed poroelastic constitutive model, which couples solid-matrix deformation to fluid transport via imaging-derived permeability, vascular fraction, and mechanical property maps inside a finite-element solver.

If this is right

  • Early MRI time points can be used to forecast later tumor volume and shape under the same mechanical and fluid conditions.
  • The same framework can be extended to incorporate the effects of chemotherapy or radiation on permeability and cell proliferation.
  • The approach supplies a quantitative basis for building patient-specific models that track both tumor mechanics and fluid pressure.
  • Agreement between predicted and observed tumor boundaries supports the use of poroelasticity rather than pure reaction-diffusion descriptions for organ-scale glioma modeling.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar MRI-to-mechanics pipelines could be applied to other infiltrative brain diseases where fluid pressure influences tissue displacement.
  • Coupling the current mechanical model to explicit cell-cycle kinetics would allow direct testing of how proliferation alters local permeability.
  • Longer validation windows or different glioma cell lines would reveal whether the current parameter ranges remain predictive beyond the five-session rat protocol.

Load-bearing premise

The poroelastic constitutive assumptions and MRI-derived maps of mechanical properties, permeability, and fluid fractions accurately capture the physical behavior of glioma tissue and surrounding brain across the observed growth period.

What would settle it

A follow-up study in which the same model, when calibrated on the first three scans of new rats, produces volume errors consistently above 36 percent or Dice scores below 0.75 on later scans would show that the poromechanical description does not capture the growth.

Figures

Figures reproduced from arXiv: 2605.05013 by Cheguye Wu, David A. Hormuth II, Giuseppe Sciume, Guillermo Lorenzo, Meryem Abbad Andaloussi, Stephane P. A. Bordas, Stephane Urcun, Thomas E. Yankeelov.

Figure 1
Figure 1. Figure 1: Definition of our three-phase model of glioma growth. (a) Components of one representa view at source ↗
Figure 2
Figure 2. Figure 2: MRI-informed anatomical geometry and model variables. (a) The computational 3D view at source ↗
Figure 3
Figure 3. Figure 3: Mesh sensitivity analysis for rats 1 and 2. For each animal, six meshes with increasing view at source ↗
Figure 4
Figure 4. Figure 4: Mesh sensitivity analysis for rats 3 and 4. For each animal, six meshes with increasing view at source ↗
Figure 5
Figure 5. Figure 5: Parameter variance-based sensitivity analysis. The tumor growth rate ( view at source ↗
Figure 7
Figure 7. Figure 7: Comparison of tumor volume observation and prediction with the vascularization field view at source ↗
Figure 6
Figure 6. Figure 6: Front views of predicted (purple) and observed (yellow) tumor evolution for all four rats. view at source ↗
Figure 8
Figure 8. Figure 8: From top to bottom: solid displacements in a coronal slice for the four rats at calibration view at source ↗
Figure 9
Figure 9. Figure 9: ε at days 3, 4 and 6 for rat 2 (a) ε t at day 3 (b) ε t at day 4 (c) ε t at day 6 view at source ↗
Figure 10
Figure 10. Figure 10: ε t at days 3, 4 and 6 for rat 4 were spatially localized. This behavior may be due to the tumor’s proximity to the boundary for rats 1 and 3, which might increase boundary-induced constraints. 3.5.3. Tumor volume fraction The tumor volume fraction increased from calibration to the first and second prediction times. Accross all animals, the maximum tumor volume fraction started at 0.42 at the initial time… view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper proposes an MRI-informed poroelastic finite-element model for C6 glioma growth in rats. Longitudinal data from n=4 animals (five time points each) supply T1-weighted images for segmentation and mechanical property assignment, DWI for tumor-fraction maps, and DCE-MRI for permeability, vascular, and liquid-fraction maps. Model parameters are calibrated by Levenberg-Marquardt minimization against the first three time points; forecasts are assessed on the final two time points via relative tumor-volume error and Dice overlap with segmentations. Reported calibration errors are 0.94–11.27 % (volume) and 0.80–0.93 (Dice); prediction errors are 4.73–36.03 % (volume) and 0.75–0.93 (Dice). The central claim is that the poromechanical model can describe glioma growth and constitutes a step toward patient-specific multiscale modeling.

Significance. If the mechanical and fluid-transport components prove essential and are shown to match observed deformation, the work would supply a rare organ-scale, imaging-constrained poroelastic framework that couples tissue mechanics, fluid flow, and tumor proliferation. The use of three distinct MRI modalities to inform spatially varying parameters and the temporal train/test split are concrete strengths that could be leveraged for therapeutic-response modeling.

major comments (3)
  1. [Results / Validation] Validation is performed exclusively on tumor-region volume and Dice scores derived from DWI tumor-fraction maps (abstract and Results). No quantitative comparison is reported between simulated displacement or pressure fields and image-registration-derived brain deformation or interstitial pressure measurements. Because the central claim concerns the poromechanical description of growth, the absence of any test of the mechanical coupling leaves the added value of the poroelastic formulation unverified.
  2. [Methods / Results] No baseline comparison (e.g., to a pure reaction-diffusion model with the same tumor-fraction data) or ablation study (mechanical coupling disabled) is presented. With only n=4 animals and volume/Dice errors that overlap the range achievable by simpler models, it remains possible that the reported agreement does not require the poroelastic constitutive relations or MRI-derived permeability maps.
  3. [Methods / Calibration] Calibration uses Levenberg-Marquardt on the first three time points; the same data also determine the MRI-derived property maps. The temporal split therefore supplies only a partial guard against overfitting, and no sensitivity analysis or parameter-uncertainty propagation is reported to quantify how calibration variability propagates into the 4.73–36.03 % prediction errors.
minor comments (2)
  1. [Abstract] The abstract states numerical ranges without error bars, standard deviations, or per-animal values; adding these would improve transparency.
  2. [Methods] Notation for the poroelastic constitutive parameters (e.g., permeability tensor, Biot coefficient) should be defined once in a dedicated table or section for reproducibility.

Simulated Author's Rebuttal

3 responses · 1 unresolved

We thank the referee for the constructive and detailed comments, which help clarify the strengths and limitations of our poromechanical modeling approach. We address each major comment point by point below, indicating revisions made to the manuscript.

read point-by-point responses

  1. Referee: [Results / Validation] Validation is performed exclusively on tumor-region volume and Dice scores derived from DWI tumor-fraction maps (abstract and Results). No quantitative comparison is reported between simulated displacement or pressure fields and image-registration-derived brain deformation or interstitial pressure measurements. Because the central claim concerns the poromechanical description of growth, the absence of any test of the mechanical coupling leaves the added value of the poroelastic formulation unverified.

    Authors: We agree that quantitative validation of the simulated displacement and pressure fields would provide stronger direct evidence for the mechanical coupling. Our dataset, however, consists solely of T1-weighted, DWI, and DCE-MRI acquisitions and does not include interstitial pressure measurements or deformation fields with sufficient ground-truth reliability for such a comparison. In the revised manuscript we have added an explicit discussion of this limitation in the Discussion section, noting that future extensions will incorporate modalities such as MR elastography. The present validation on tumor volume and spatial overlap remains the primary clinical endpoint, while the poroelastic formulation ensures that growth predictions are mechanically consistent with the MRI-derived tissue properties. revision: partial

  2. Referee: [Methods / Results] No baseline comparison (e.g., to a pure reaction-diffusion model with the same tumor-fraction data) or ablation study (mechanical coupling disabled) is presented. With only n=4 animals and volume/Dice errors that overlap the range achievable by simpler models, it remains possible that the reported agreement does not require the poroelastic constitutive relations or MRI-derived permeability maps.

    Authors: We acknowledge the utility of a baseline comparison. In the revised manuscript we have added a new Results subsection that implements and evaluates a simplified reaction-diffusion model calibrated and validated on identical tumor-fraction data and the same temporal split. The comparison shows modestly lower average prediction errors for the poroelastic model, especially in animals exhibiting noticeable tissue deformation. We also discuss the contribution of the MRI-derived permeability and mechanical-property maps. The limited cohort size (n=4) is typical for intensive longitudinal imaging studies; individual animal results are reported to permit direct assessment of variability. revision: yes

  3. Referee: [Methods / Calibration] Calibration uses Levenberg-Marquardt on the first three time points; the same data also determine the MRI-derived property maps. The temporal split therefore supplies only a partial guard against overfitting, and no sensitivity analysis or parameter-uncertainty propagation is reported to quantify how calibration variability propagates into the 4.73–36.03 % prediction errors.

    Authors: We agree that sensitivity and uncertainty analyses would strengthen the calibration section. We have performed a one-at-a-time sensitivity study on the principal calibrated parameters (proliferation rate, hydraulic permeability, and elastic moduli) and included the resulting prediction-error ranges in the Supplementary Material. The MRI-derived property maps are fixed from the imaging data and are not optimized during calibration; the temporal train/test split therefore evaluates forward prediction. A short paragraph on calibration robustness has been added to the Methods section of the revision. revision: yes

standing simulated objections not resolved
  • Quantitative comparison of simulated displacement or pressure fields against experimental measurements, because the imaging protocol did not acquire interstitial pressure data or deformation ground truth suitable for such validation.

Circularity Check

0 steps flagged

No significant circularity in derivation or validation chain

full rationale

The paper performs parameter calibration via Levenberg-Marquardt on the first three MRI time points per animal and evaluates forecasts on the held-out later two time points, using relative tumor volume error and Dice overlap as metrics. This constitutes a standard temporal train-test split with independent data, not a reduction of predictions to calibration inputs by construction. MRI-derived maps inform constitutive parameters and initial conditions, but the forward simulation and validation metrics remain distinct from the fitting procedure. No self-definitional equations, load-bearing self-citations, or ansatz smuggling are present in the described chain; the central claim rests on external data agreement rather than tautological re-expression of inputs.

Axiom & Free-Parameter Ledger

3 free parameters · 2 axioms · 0 invented entities

The central claim rests on standard continuum poroelasticity, the fidelity of MRI-derived tissue maps, and parameters fitted to early time-point data; no new physical entities are postulated.

free parameters (3)
  • mechanical properties
    Assigned from T1-weighted segmentation for brain and tumor regions
  • permeability, vascular and liquid fractions
    Derived from dynamic contrast-enhanced MRI maps
  • model parameters
    Calibrated by Levenberg-Marquardt on first three MRI time points per animal
axioms (2)
  • domain assumption Poroelastic continuum mechanics governs brain and tumor deformation and fluid transport
    Invoked to justify the finite-element formulation
  • domain assumption MRI signals accurately reflect local tumor fraction, permeability, and fluid content
    Basis for informing model inputs from imaging

pith-pipeline@v0.9.0 · 5654 in / 1534 out tokens · 51944 ms · 2026-05-08T15:52:14.294355+00:00 · methodology

discussion (0)

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