Forecasting chemoradiation response mid-treatment for high-grade gliomas through patient-specific biology-based modeling

Abstract

Purpose: The entire course of radiotherapy (RT) for high-grade glioma (HGG) is currently derived from pre-RT MRI. While it is possible to adapt RT during the course of treatment, it is often guided only by anatomical changes to the tumor. This study seeks to determine if a biology-based mathematical model, parameterized by patient-specific, multi-parametric magnetic resonance imaging (mpMRI) data, can accurately forecast HGG response during RT.

Methods and materials: 21 patients with HGG planned for 6 weeks of concurrent RT and chemotherapy were imaged weekly with mpMRI during RT and at 1-month, 2-month, 3-months post-RT. Each patient's MRI data from baseline to mid-treatment was used to personalize a family of biology-based mathematical models, from which the most parsimonious was selected and used to predict response at the volume and voxel levels at the remaining mpMRI visits. The model family consists of varied descriptions of how tumor cells proliferate, diffuse, and respond to RT and chemotherapy.

Results: At the volume level, Pearson correlation coefficients (PCC) greater than 0.86 (p < 0.0001) were observed between the predicted and observed total tumor cellularity and volume up to the 2-month post-RT. A high-level of spatial overlap was measured between the predicted and observed tumor extent with Dice values greater than 0.87 and 0.74 during and following RT, respectively. At the voxel level, PCCs were greater than 0.90 and 0.71 (p < 0.0001) during and following RT, respectively.

Conclusions: By leveraging patient-specific mpMRI data before and during adaptive RT, this biology-based computational framework yields accurate spatiotemporal forecasts of tumor response at the volume and voxel levels during and following RT.

Keywords: brain cancer; diffusion weighted MRI; mathematical oncology; precision oncology; reaction diffusion.