https://sam.ensam.eu:443 The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material. Tue, 09 Jun 2026 20:37:58 GMT 2026-06-09T20:37:58Z 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.