HER2+ breast cancer patients, as observed by oncologist Agnes Jager from Erasmus Medical Centre (EMC), often achieve radiologic complete response (rCR) earlier than expected under standard treatments. To address this, Jager has partnered with Delft University of Technology to develop a computational model aimed at personalizing treatment schedules, potentially reducing chemotherapy cycles and minimizing side effects.

Building on previous MSc theses by Nathalie Oudhof, Eva Slingerland, and Rutger Engelberts, this thesis aims to improve the predictive capability of the Drug-Induced Mechanically Coupled Reaction-Diffusion (DI-MRCD) model and test its performance on a larger dataset consisting of 13 patients. The DI-MRCD model combines dynamic contrast-enhanced (DCE) and diffusion-weighted imaging (DWI) magnetic resonance imaging (MRI) data with patient-specific parameters to simulate the reaction of breast cancer tumours on chemotherapy. Key improvements include optimizing and generalizing the pre-processing pipeline for a larger patient cohort and enhancing input reliability by independently computing apparent diffusion coefficients (ADC).

Further refinements to the DI-MRCD model include updates to chemotherapy and shear modulus parameters, switching to a Trust Region Reflective (TRF) optimization method, and introducing a tissue-specific proliferation rate and natural cell death term. Despite these enhancements providing more insight, control, and making the model biologically more realistic, the model struggled to converge, highlighting the challenge of fitting patient-specific parameters with limited data points.

Future improvements could include resolving the convergence issues, incorporating additional calibration parameters, allowing the proliferation rate to travel with tumour cells as they diffuse, incorporating chemotherapy doses into the chemotherapy term, using Bayesian optimization for better parameter estimation, and making the results more explanatory by integrating other patient data.