Deep Learning Characterization of Brain Tumours With Diffusion Weighted Imaging

Abstract

Glioblastoma multiforme (GBM) is one of the most deadly forms of cancer. Methods of characterizing these tumours are valuable for improving predictions of their progression and response to treatment. A mathematical model called the proliferation-invasion (PI) model has been used extensively in the literature to model these tumours, though it relies on known values of two key parameters: the tumour cell diffusivity and proliferation rate. Unfortunately, these parameters are difficult to estimate in a patient-specific manner, making personalized tumour projections challenging. In this paper, we develop and apply a deep learning model capable of making accurate estimates of these key GBM-characterizing parameters while simultaneously producing a full projection of the tumour progression curve. Our method uses two sets of multi sequence MRI imaging in order to make predictions and relies on a preprocessing pipeline which includes brain tumour segmentation and conversion to tumour cellularity. We apply our deep learning model to both synthetic tumours and a dataset consisting of five patients diagnosed with GBM. For all patients, we derive evidence-based estimates for each of the PI model parameters and predictions for the future progression of the tumour. Discussion and implications for future work and clinical relevance are included.