  Reliably predicting the future spread of brain tumors using imaging data and
on a subject-specific basis requires quantifying uncertainties in data,
biophysical models of tumor growth, and spatial heterogeneity of tumor and host
tissue. This work introduces a Bayesian framework to calibrate the spatial
distribution of the parameters within a tumor growth model to quantitative
magnetic resonance imaging (MRI) data and demonstrates its implementation in a
pre-clinical model of glioma. The framework leverages an atlas-based brain
segmentation of grey and white matter to establish subject-specific priors and
tunable spatial dependencies of the model parameters in each region. Using this
framework, the tumor-specific parameters are calibrated from quantitative MRI
measurements early in the course of tumor development in four rats and used to
predict the spatial development of the tumor at later times. The results
suggest that the tumor model, calibrated by animal-specific imaging data at one
time point, can accurately predict tumor shapes with a Dice coefficient > 0.89.
However, the reliability of the predicted volume and shape of tumors strongly
relies on the number of earlier imaging time points used for calibrating the
model. This study demonstrates, for the first time, the ability to determine
the uncertainty in the inferred tissue heterogeneity and the model predicted
tumor shape.
