Cortex tissue relaxation and slow to medium load rates dependency can be captured by a two-phase flow poroelastic model

Urcun, Stéphane; Rohan, Pierre-Yves; Sciumè, G.; Bordas, Stéphane

doi:10.1016/j.jmbbm.2021.104952

Cited by 10 publications

(19 citation statements)

References 52 publications

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“…General mechanical parameters of cortex tissue. were prescribed our previous work [41]. In [41], we reproduced two mechanical tests on healthy human and animal cortex: confined compression (N = 6), i.e.…”

Section: Initial Parameters Settingsmentioning

confidence: 99%

“…were prescribed our previous work [41]. In [41], we reproduced two mechanical tests on healthy human and animal cortex: confined compression (N = 6), i.e. consolidation tests of Franceschini et al [59] and unconfined compression (N = 40), i.e.…”

Section: Initial Parameters Settingsmentioning

confidence: 99%

“…Without clinical measurement, the minimum vascular fraction required to convey the effect of TMZ is set to ω bs T M Z = 0.006, this value corresponds to twice the minimal vascular fraction. These parameters are summarized in [41]. For the sources of the literature values, see [41].…”

Section: Initial Parameters Settingsmentioning

confidence: 99%

“…For the solid scaffold deformation, the chosen clo-sure relationship for the effective stress is linear elastic: with the identity tensor, the linearised strain tensor, and the Lamé constant and . The two parameters E (except the stiffened E ECM ) and ν were calibrated by ex vivo mechanical testing in [41].…”

Section: Introductionmentioning

confidence: 99%

“…The three fluid phases have their own relative permeabilities: with A l and B g , the exponents governing the evolution of the relative permeabilities. Both were calibrated by ex vivo mechanical testing in [41]. We choose to apply B g to both glial and glioma cells.…”

Section: Introductionmentioning

confidence: 99%

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Non-operable glioblastoma: proposition of patient-specific forecasting by image-informed poromechanical model

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We propose a novel image-informed glioblastoma mathematical model within a reactive multiphase poromechanical framework. Poromechanics offers to model in a coupled manner the interplay between tissue deformation and pressure-driven fluid flows, these phenomena existing simultaneously in cancer disease. The model also relies on two mechano-biological hypotheses responsible for the heterogeneity of the GBM: hypoxia signaling cascade and interaction between extra-cellular matrix and tumor cells. The model belongs to the category of patient-specific image-informed models as it is initialized, calibrated and evaluated by the means of patient imaging data. The model is calibrated with patient data after 6 cycles of concomitant radiotherapy chemotherapy and shows good agreement with treatment response 3 months after chemotherapy maintenance. Sensitivity of the solution to parameters and to boundary conditions is provided. As this work is only a first step of the inclusion of poromechanical framework in image-informed glioblastoma mathematical models, leads of improvement are provided in the conclusion.

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Section: Initial Parameters Settingsmentioning

confidence: 99%

Section: Initial Parameters Settingsmentioning

confidence: 99%

Section: Initial Parameters Settingsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Non-operable glioblastoma: proposition of patient-specific forecasting by image-informed poromechanical model

Urcun

Baroli

Rohan

et al. 2023

Preprint

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Sonics: develop intuition on biomechanical systems through interactive error controlled simulations

Mazier,

El Hadramy,

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et al. 2023

Engineering with Computers

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We describe the SOniCS (SOFA + FEniCS) plugin to help develop an intuitive understanding of complex biomechanics systems. This new approach allows the user to experiment with model choices easily and quickly without requiring in-depth expertise. Constitutive models can be modified by one line of code only. This ease in building new models makes SOniCS ideal to develop surrogate, reduced order models and to train machine-learning algorithms for enabling real-time patient-specific simulations. SOniCS is thus not only a tool that facilitates the development of surgical training simulations but also, and perhaps more importantly, paves the way to increase the intuition of users or otherwise non-intuitive behaviors of (bio)mechanical systems. The plugin uses new developments of the FEniCSx project enabling automatic generation with FFCx of finite-element tensors, such as the local residual vector and Jacobian matrix. We verify our approach with numerical simulations, such as manufactured solutions, cantilever beams, and benchmarks provided by FEBio. We reach machine precision accuracy and demonstrate the use of the plugin for a real-time haptic simulation involving a surgical tool controlled by the user in contact with a hyperelastic liver. We include complete examples showing the use of our plugin for simulations involving Saint Venant–Kirchhoff, Neo-Hookean, Mooney–Rivlin, and Holzapfel Ogden anisotropic models as supplementary material.

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From digital control to digital twins in medicine: A brief review and future perspectives

Eftimie

Mavrodin

Bordas

2023

Advances in Applied Mechanics

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Cortex tissue relaxation and slow to medium load rates dependency can be captured by a two-phase flow poroelastic model

Cited by 10 publications

References 52 publications

Non-operable glioblastoma: proposition of patient-specific forecasting by image-informed poromechanical model

Non-operable glioblastoma: proposition of patient-specific forecasting by image-informed poromechanical model

Sonics: develop intuition on biomechanical systems through interactive error controlled simulations

From digital control to digital twins in medicine: A brief review and future perspectives

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