| Summary: | Predicting tumour growth and its response to therapy remains a major challenge in cancer research and strongly relies on tumour growth models. In this work, we introduce, calibrate and verify a novel image-driven reaction-diffusion model of avascular tumour growth. The model allows for proliferation, death and spread of tumour cells, and accounts for nutrient distribution and hypoxia. It is constrained by longitudinal time series of DCE-MRI images. Tumour specific parameters are estimated from two early time points and used to predict the spatio-temporal evolution of the tumour volume and cell densities at later time points. We first test our parameter estimation approach on synthetic data from 15 generated tumours. Our in silico study resulted in small volume errors (< 5%) and high Dice overlaps (>97%), showing that model parameters can be successfully recovered and used to accurately predict tumour growth. Encouraged by these results, we apply our model to seven pre-clinical cases of breast carcinoma. We are able to show promising preliminary results, especially for the estimation for early time points. Processes like angiogenesis and apoptosis should be included to further improve predictions for later time points.
|
|---|