https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Wed, 13 May 2026 16:55:27 GMT 2026-05-13T16:55:27Z http://hdl.handle.net/10985/25443 Single and bi-compartment poro-elastic model of perfused biological soft tissues: FEniCSx implementation and tutorial LAVIGNE, Thomas; URCUN, Stéphane; ROHAN, Pierre-Yves; SCIUME, Giuseppe; BAROLI, Davide; BORDAS, Stéphane Pierre Alain Soft biological tissues demonstrate strong time-dependent and strain-rate mechanical behavior, arising from their intrinsic visco-elasticity and fluid–solid interactions. The time-dependent mechanical properties of soft tissues influence their physiological functions and are related to several pathological processes. Poro-elastic modeling represents a promising approach because it allows the integration of multiscale/multiphysics data to probe biologically relevant phenomena at a smaller scale and embeds the relevant mechanisms at the larger scale. The implementation of multiphase flow poro-elastic models however is a complex undertaking, requiring extensive knowledge. The open-source software FEniCSx Project provides a novel tool for the automated solution of partial differential equations by the finite element method. This paper aims to provide the required tools to model the mixed formulation of poro-elasticity, from the theory to the implementation, within FEniCSx. Several benchmark cases are studied. A column under confined compression conditions is compared to the Terzaghi analytical solution, using the L2-norm. An implementation of poro-hyper-elasticity is proposed. A bi-compartment column is compared to previously published results (Cast3m implementation). For all cases, accurate results are obtained in terms of a normalized Root Mean Square Error (RMSE). Furthermore, the FEniCSx computation is found three times faster than the legacy FEniCS one. The benefits of parallel computation are also highlighted. This research was funded in whole, or in part, by the Luxembourg National Research Fund (FNR), grant reference No. 17013182. Mon, 01 May 2023 00:00:00 GMT http://hdl.handle.net/10985/25443 2023-05-01T00:00:00Z LAVIGNE, Thomas URCUN, Stéphane ROHAN, Pierre-Yves SCIUME, Giuseppe BAROLI, Davide BORDAS, Stéphane Pierre Alain Soft biological tissues demonstrate strong time-dependent and strain-rate mechanical behavior, arising from their intrinsic visco-elasticity and fluid–solid interactions. The time-dependent mechanical properties of soft tissues influence their physiological functions and are related to several pathological processes. Poro-elastic modeling represents a promising approach because it allows the integration of multiscale/multiphysics data to probe biologically relevant phenomena at a smaller scale and embeds the relevant mechanisms at the larger scale. The implementation of multiphase flow poro-elastic models however is a complex undertaking, requiring extensive knowledge. The open-source software FEniCSx Project provides a novel tool for the automated solution of partial differential equations by the finite element method. This paper aims to provide the required tools to model the mixed formulation of poro-elasticity, from the theory to the implementation, within FEniCSx. Several benchmark cases are studied. A column under confined compression conditions is compared to the Terzaghi analytical solution, using the L2-norm. An implementation of poro-hyper-elasticity is proposed. A bi-compartment column is compared to previously published results (Cast3m implementation). For all cases, accurate results are obtained in terms of a normalized Root Mean Square Error (RMSE). Furthermore, the FEniCSx computation is found three times faster than the legacy FEniCS one. The benefits of parallel computation are also highlighted. http://hdl.handle.net/10985/25444 Non-operable glioblastoma: Proposition of patient-specific forecasting by image-informed poromechanical model URCUN, Stéphane; BAROLI, Davide; ROHAN, Pierre-Yves; SKALLI, Wafa; LUBRANO, Vincent; BORDAS, Stéphane Pierre Alain; SCIUME, Giuseppe 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. Statement of significance: In this study, we employ mechanics of reactive porous media to effectively model the dynamic progression of a glioblastoma. Traditionally, glioblastoma tumors are surgically removed a few weeks post-diagnosis. To address this, we focus on a non-operable clinical scenario which allows us to have sufficient time points for the calibration and subsequent validation of our mathematical model. It is paramount to underscore that the tumor’s evolution is significantly influenced by chemotherapy and radiotherapy. These therapeutic effects find incorporation within our mathematical framework. Notably, the approach we present is distinctive for two key reasons: Firstly, the mathematical model inherently captures the complex multiphase and hierarchical nature of brain tissue. Secondly, our constitutive laws factor in the ever-changing properties of cells and tissues, mirroring the local phenotypic alterations observed within the tumor. This work constitutes an initial stride towards systematically integrating multiphase poromechanics into patient-specific glioblastoma growth modeling. As we look ahead, we acknowledge areas for potential enhancement in pursuit of advancing this promising direction. Work funding with a grant fromLuxembourg National Research Fund (FNR) grant number INTER/ANR/21/16399490 and from Réseau Santé des Arts et Métiers. Wed, 01 Mar 2023 00:00:00 GMT http://hdl.handle.net/10985/25444 2023-03-01T00:00:00Z URCUN, Stéphane BAROLI, Davide ROHAN, Pierre-Yves SKALLI, Wafa LUBRANO, Vincent BORDAS, Stéphane Pierre Alain SCIUME, Giuseppe 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. Statement of significance: In this study, we employ mechanics of reactive porous media to effectively model the dynamic progression of a glioblastoma. Traditionally, glioblastoma tumors are surgically removed a few weeks post-diagnosis. To address this, we focus on a non-operable clinical scenario which allows us to have sufficient time points for the calibration and subsequent validation of our mathematical model. It is paramount to underscore that the tumor’s evolution is significantly influenced by chemotherapy and radiotherapy. These therapeutic effects find incorporation within our mathematical framework. Notably, the approach we present is distinctive for two key reasons: Firstly, the mathematical model inherently captures the complex multiphase and hierarchical nature of brain tissue. Secondly, our constitutive laws factor in the ever-changing properties of cells and tissues, mirroring the local phenotypic alterations observed within the tumor. This work constitutes an initial stride towards systematically integrating multiphase poromechanics into patient-specific glioblastoma growth modeling. As we look ahead, we acknowledge areas for potential enhancement in pursuit of advancing this promising direction. http://hdl.handle.net/10985/25521 Oncology and mechanics: Landmark studies and promising clinical applications URCUN, Stéphane; LORENZO, Guillermo; BAROLI, Davide; ROHAN, Pierre-Yves; SCIUME, Giuseppe; SKALLI, Wafa; LUBRANO, Vincent; BORDAS, Stéphane Pierre Alain Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular, and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design, and therapeutic targets. Additionally, a specific focus is placed on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling. Wed, 01 Jun 2022 00:00:00 GMT http://hdl.handle.net/10985/25521 2022-06-01T00:00:00Z URCUN, Stéphane LORENZO, Guillermo BAROLI, Davide ROHAN, Pierre-Yves SCIUME, Giuseppe SKALLI, Wafa LUBRANO, Vincent BORDAS, Stéphane Pierre Alain Clinical management of cancer has continuously evolved for several decades. Biochemical, molecular, and genomics approaches have brought and still bring numerous insights into cancerous diseases. It is now accepted that some phenomena, allowed by favorable biological conditions, emerge via mechanical signaling at the cellular scale and via mechanical forces at the macroscale. Mechanical phenomena in cancer have been studied in-depth over the last decades, and their clinical applications are starting to be understood. If numerous models and experimental setups have been proposed, only a few have led to clinical applications. The objective of this contribution is to review a large scope of mechanical findings which have consequences on the clinical management of cancer. This review is mainly addressed to doctoral candidates in mechanics and applied mathematics who are faced with the challenge of the mechanics-based modeling of cancer with the aim of clinical applications. We show that the collaboration of the biological and mechanical approaches has led to promising advances in terms of modeling, experimental design, and therapeutic targets. Additionally, a specific focus is placed on imaging-informed mechanics-based models, which we believe can further the development of new therapeutic targets and the advent of personalized medicine. We study in detail several successful workflows on patient-specific targeted therapies based on mechanistic modeling. http://hdl.handle.net/10985/17105 Quantifying discretization errors for soft tissue simulation in computer assisted surgery: A preliminary study DUPREZ, Michel; BORDAS, Stéphane Pierre Alain; BUCKI, Marek; BUI, Huu Phuoc; CHOULY, Franz; LLERAS, Vanessa; LOBOS, Claudio; LOZINSKI, Alexei; TOMAR, Satyendra; ROHAN, Pierre-Yves Errors in biomechanics simulations arise from modelling and discretization. Modelling errors are due to the choice of the mathematical model whilst discretization errors measure the impact of the choice of the numerical method on the accuracy of the approximated solution to this specific mathematical model. A major source of discretization errors is mesh generation from medical images, that remains one of the major bottlenecks in the development of reliable, accurate, automatic and efficient personalized, clinically-relevant Finite Element (FE) models in biomechanics. The impact of mesh quality and density on the accuracy of the FE solution can be quantified with a posteriori error estimates. Yet, to our knowledge, the relevance of such error estimates for practical biomechanics problems has seldom been addressed, see Bui et al. (2018). In this contribution, we propose an implementation of some a posteriori error estimates to quantify the discretization errors and to optimize the mesh. More precisely, we focus on error estimation for a user-defined quantity of interest with the Dual Weighted Residual (DWR) technique. We test its applicability and relevance in three situations, corresponding to experiments in silicone samples and computations for a tongue and an artery, using a simplified setting, i.e., plane linearized elasticity with contractility of the soft tissue modeled as a pre-stress. Our results demonstrate the feasibility of such methodology to estimate the actual solution errors and to reduce them economically through mesh refinement. Wed, 01 Jan 2020 00:00:00 GMT http://hdl.handle.net/10985/17105 2020-01-01T00:00:00Z DUPREZ, Michel BORDAS, Stéphane Pierre Alain BUCKI, Marek BUI, Huu Phuoc CHOULY, Franz LLERAS, Vanessa LOBOS, Claudio LOZINSKI, Alexei TOMAR, Satyendra ROHAN, Pierre-Yves Errors in biomechanics simulations arise from modelling and discretization. Modelling errors are due to the choice of the mathematical model whilst discretization errors measure the impact of the choice of the numerical method on the accuracy of the approximated solution to this specific mathematical model. A major source of discretization errors is mesh generation from medical images, that remains one of the major bottlenecks in the development of reliable, accurate, automatic and efficient personalized, clinically-relevant Finite Element (FE) models in biomechanics. The impact of mesh quality and density on the accuracy of the FE solution can be quantified with a posteriori error estimates. Yet, to our knowledge, the relevance of such error estimates for practical biomechanics problems has seldom been addressed, see Bui et al. (2018). In this contribution, we propose an implementation of some a posteriori error estimates to quantify the discretization errors and to optimize the mesh. More precisely, we focus on error estimation for a user-defined quantity of interest with the Dual Weighted Residual (DWR) technique. We test its applicability and relevance in three situations, corresponding to experiments in silicone samples and computations for a tongue and an artery, using a simplified setting, i.e., plane linearized elasticity with contractility of the soft tissue modeled as a pre-stress. Our results demonstrate the feasibility of such methodology to estimate the actual solution errors and to reduce them economically through mesh refinement. http://hdl.handle.net/10985/21320 Digital twinning of Cellular Capsule Technology: Emerging outcomes from the perspective of porous media mechanics URCUN, Stéphane; SKALLI, Wafa; NASSOY, Pierre; BORDAS, Stéphane Pierre Alain; SCIUMÈ, Giuseppe; ROHAN, Pierre-Yves Spheroids encapsulated within alginate capsules are emerging as suitable in vitro tools to investigate the impact of mechanical forces on tumor growth since the internal tumor pressure can be retrieved from the deformation of the capsule. Here we focus on the particular case of Cellular Capsule Technology (CCT). We show in this contribution that a modeling approach accounting for the triphasic nature of the spheroid (extracellular matrix, tumor cells and interstitial fluid) offers a new perspective of analysis revealing that the pressure retrieved experimentally cannot be interpreted as a direct picture of the pressure sustained by the tumor cells and, as such, cannot therefore be used to quantify the critical pressure which induces stress-induced phenotype switch in tumor cells. The proposed multiphase reactive poro-mechanical model was cross-validated. Parameter sensitivity analyses on the digital twin revealed that the main parameters determining the encapsulated growth configuration are different from those driving growth in free condition, confirming that radically different phenomena are at play. Results reported in this contribution support the idea that multiphase reactive poro-mechanics is an exceptional theoretical framework to attain an in-depth understanding of CCT experiments, to confirm their hypotheses and to further improve their design. Fri, 01 Jan 2021 00:00:00 GMT http://hdl.handle.net/10985/21320 2021-01-01T00:00:00Z URCUN, Stéphane SKALLI, Wafa NASSOY, Pierre BORDAS, Stéphane Pierre Alain SCIUMÈ, Giuseppe ROHAN, Pierre-Yves Spheroids encapsulated within alginate capsules are emerging as suitable in vitro tools to investigate the impact of mechanical forces on tumor growth since the internal tumor pressure can be retrieved from the deformation of the capsule. Here we focus on the particular case of Cellular Capsule Technology (CCT). We show in this contribution that a modeling approach accounting for the triphasic nature of the spheroid (extracellular matrix, tumor cells and interstitial fluid) offers a new perspective of analysis revealing that the pressure retrieved experimentally cannot be interpreted as a direct picture of the pressure sustained by the tumor cells and, as such, cannot therefore be used to quantify the critical pressure which induces stress-induced phenotype switch in tumor cells. The proposed multiphase reactive poro-mechanical model was cross-validated. Parameter sensitivity analyses on the digital twin revealed that the main parameters determining the encapsulated growth configuration are different from those driving growth in free condition, confirming that radically different phenomena are at play. Results reported in this contribution support the idea that multiphase reactive poro-mechanics is an exceptional theoretical framework to attain an in-depth understanding of CCT experiments, to confirm their hypotheses and to further improve their design.