Speaker
Description
Tumour growth and treatment response emerge from coupled interactions between mechanics, hypoxia, and drug transport, yet most phase-field models remain purely forward with fixed parameters. This limits their ability to capture tumour-specific variability.
We develop a mechanochemically coupled phase-field model integrating tumour evolution, oxygen dynamics, paclitaxel transport, and quasi-static elasticity. Tumour dynamics are described using an Allen–Cahn formulation to reflect non-conservative mass under therapy, with a modified Cahn–Hilliard model used for comparison. The model incorporates oxygen-limited proliferation, hypoxia-induced death, drug cytotoxicity, and stress-mediated inhibition.
Simulations reproduce key spatial features, including hypoxic cores and drug exclusion regions, arising from feedback between stress, oxygen depletion, and transport.
To enable predictive modelling, we embed the system within a PDE-constrained inverse framework to recover tumour-specific parameters from imaging data. Ongoing work focuses on adjoint-based optimisation, identifiability analysis, and stress-dependent boundary conditions.
This framework provides a pathway toward personalised prediction of tumour response to therapy.
