| Abstract: |
| In this work we aim to explore oedema infiltration and predict relapse patterns of GBM. To address this, we
propose a novel multiscale mathematical modelling framework to simulate and predict tumour growth, oedema
infiltration, and treatment response under various conditions. Simulation results obtained by exploring a large
space of post-operatory residual oedema cell distributions led us to formulate the hypothesis that a higher
concentration of tumour cells remaining near the surgical cavity edge led to slower and more localized tumour
growth. Based on this simulations-inspired hypothesis we explore the ways of reconstructing the personalised
initial tumour distribution within the oedema from existing MRI patient data in an inverse problem approach, with
the ultimate goal of achieving prediction abilities for our modelling framework. The prediction abilities acquired
by our framework through this inverse problem approach are promising, which for instance enabled us to achieve
realistic prediction (i.e., match MRI data) of 881 days post-treatment GBM relapse evolution [4].
While further analytical investigations are ongoing, this innovative approach holds promise for reconstructing
tumor relapses from readily available clinical data, offering new insights into GBM progression and treatment
response. |
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