Deep Learning for Reaction-Diffusion Glioma Growth Modelling: Towards a Fully Personalised Model?

TL;DR

This paper demonstrates that deep convolutional neural networks can accurately estimate glioma growth parameters and tumor evolution from limited MRI data, potentially enabling personalized clinical modeling.

Contribution

It introduces a deep learning approach to estimate reaction-diffusion model parameters and tumor evolution from minimal imaging data, addressing previous estimation challenges.

Findings

DCNNs accurately reconstruct tumor cell density from two contours.

They estimate individual diffusivity and proliferation parameters from a prior time point.

The method successfully applies to real patient MRI data.

Abstract

Reaction-diffusion models have been proposed for decades to capture the growth of gliomas, the most common primary brain tumours. However, severe limitations regarding the estimation of the initial conditions and parameter values of such models have restrained their clinical use as a personalised tool. In this work, we investigate the ability of deep convolutional neural networks (DCNNs) to address the pitfalls commonly encountered in the field. Based on 1,200 synthetic tumours grown over real brain geometries derived from magnetic resonance (MR) data of 6 healthy subjects, we demonstrate the ability of DCNNs to reconstruct a whole tumour cell density distribution from only two imaging contours at a single time point. With an additional imaging contour extracted at a prior time point, we also demonstrate the ability of DCNNs to accurately estimate the individual diffusivity and proliferation parameters of the model. From this knowledge, the spatio-temporal evolution of the tumour cell density distribution at later time points can ultimately be precisely captured using the model. We finally show the applicability of our approach to MR data of a real glioblastoma patient. This approach may open the perspective of a clinical application of reaction-diffusion growth models for tumour prognosis and treatment planning.