We presented our computational framework (GlimSLib) for estimating growth and mechanical coupling parameters at the annual meeting of the European Society of Biomechanics. The abstract is available here.
We presented a first evaluation of our computational framework (GlimSLib) for estimating GBM growth (and mechanical coupling) parameters from clinical images at the Physical Sciences in Oncology Mathematical Oncology meeting. The abstract will be available here shortly.
We presented two posters at the 23rd Annual Scientific Meeting of the Society for Neuro-Oncology that investigate the robustness of quantitative measures of tumor mass-effect and the relation between mass-effect and advanced MR-imaging characteristics of the tumor micro-environment. Find the posters here and here.
We presented an approach for image-based parameter optimization of a mechanically-coupled tumor growth model at the 8th World Congress of Biomechanics (WCB). Check out the poster.
We presented results of an in-silico study into the effects of tissue anisotropy on brain tumor growth at the 15th International Symposium on Computer Methods in Biomechanics and Biomedical Engineering (CMBBE 2018). Abstract and manuscript are now available online.
We participated in the user and developer conference of the FEniCS project (FEniCS’18) to present the GlimS use-case and first steps towards an image-based parameter estimation approach based on FEniCS and dolfin-adjoint. The abstract is available in the online program (Session 6: Adjoints & PDE constrained optimization, Simulating Brain Tumor Mass-Effect).
An extended version of our mechanically-coupled tumor growth model was presented at the 2017 Annual Meeting of the Society for NeuroOncology (SNO). Here we incorporate brain tissue anisotropy inferred from MR-DTI imaging information to improve tumor shape prediction. See abstract and poster online.