Evaluating Glioma Growth Predictions as a Forward Ranking Problem

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 applying a biophysical tumor growth model to 21 patient cases we compare two schemes for fitting and evaluating predictions. By carefully designing a scheme that separates the prediction from the observations used for fitting the model, we show that a better fit of model parameters does not guarantee a better predictive power.