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See detailTowards real-time patient-specific breast simulations: from full-field information to surrogate model
Mazier, Arnaud UL; Lavigne, Thomas UL; Lengiewicz, Jakub UL et al

Scientific Conference (2022, July)

In breast cancer treatment, surgery is one of the most common practices [DeSantis et al., 2019]. The surgery involves a complex pipeline, principally due to the difference between the imaging and the ... [more ▼]

In breast cancer treatment, surgery is one of the most common practices [DeSantis et al., 2019]. The surgery involves a complex pipeline, principally due to the difference between the imaging and the surgical posture [Mazier et al., 2021]. Indeed, because of the stance difference, the surgeon has to rely on radioactive or invasive markers to predict the tumor position in the surgical setup. Biomechanical simulations could predict such complex tumor displacements but often require patient-specific data (material properties, organs geometries, or loading and boundary conditions). Full-field acquisitions coupled with landmark identifications allow obtaining relative deformation between the different configurations. Having this information and assuming a finite element model, an identification procedure of the model parameters can be carried out. Finally, finding a suitable computational model allowing for a compromise between accuracy and speed, one may consider surrogate models for real-time simulations (20 to 50 FPS). In this work, we obtained the patient-specific geometry through micro-computed tomography in 8 different configurations, including 15 bio-markers. Assessing the displacement of the bio-markers enabled us to infer the relative strains between the different configurations. A heterogeneous neo-Hookean model was assumed for simulating soft tissue behavior. Based on the displacements and the position of the biomarkers, model parameters identification was performed to calibrate the experimental data with the finite element method results. To overcome speed performance issues, Convolutional Neural Network (CNN) trained with a synthetic simulation-based dataset generated by applying different gravity directions is used. Preliminary results show that CNN can predict the displacement of anatomical landmarks to millimetric precision and is 100 times faster than the finite element method, satisfying our real-time objective. Plus, the use of Bayesian inferences involves a longer prediction time but allows a 95% confidence interval of the biomarkers' displacements. For a given precision, contrary to CNNs, optimization methods are computationally expensive and depend on an initialization point. Although CNNs require new training for each patient, optimization algorithms can be applied regardless of the patient's geometry. Through this study, we observed that material properties were playing an essential role but not as much as the anatomical structures e.g. infra-mammary or Copper’s ligaments. [less ▲]

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See detailMECHANO-BIOLOGY OF TUMOR GROWTH WITH THE AIM OF CLINICAL APPLICATIONS, A REACTIVE MULTIPHASE POROMECHANICAL APPROACH
Urcun, Stephane UL

Doctoral thesis (2022)

We propose the modeling of glioblastoma isocitrate dehydrogenase wild-type (GBMwt) build on the following hypotheses: the brain tissue is a porous medium, the coupling of hypoxia consequences and ... [more ▼]

We propose the modeling of glioblastoma isocitrate dehydrogenase wild-type (GBMwt) build on the following hypotheses: the brain tissue is a porous medium, the coupling of hypoxia consequences and mechanical interplay between extra-cellular matrix and tumor cells is the driver of the malignant evolution of the disease. In this thesis, a poromechanical model is developed with the aim of a clinical application in oncology. A review, with a large scope, is done on mechanical applications in clinical management of cancer. The model is first validated on in vitro experimental data of encapsulated multi-cellular spheroids. Then, a clinical collaboration is initiated with the Neuro-imaging center of Toulouse, and the targeted clinical application is the modeling of non- operable GBMwt. To this end, the model is first adapted to the specificity of brain tissue mechanics. Characteristic features of the disease are modeled: necrotic core, modified extra-cellular matrix production, emerging malignant phenotype and invasion. Clinical imaging data are pre- treated to inform the model in a patient specific basis. A proposition of modeling is provided with an evaluation against clinical data. [less ▲]

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