  The problem of tumor growth prediction is challenging, but promising results
have been achieved with both model-driven and statistical methods. In this
work, we present a framework for the evaluation of growth predictions that
focuses on the spatial infiltration patterns, and specifically evaluating a
prediction of future growth. We propose to frame the problem as a ranking
problem rather than a segmentation problem. Using the average precision as a
metric, we can evaluate the results with segmentations while using the full
spatiotemporal prediction. Furthermore, by separating the model goodness-of-fit
from future predictive performance, we show that in some cases, a better fit of
model parameters does not guarantee a better the predictive power.
