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.
Cite
@article{arxiv.2103.11651,
title = {Evaluating glioma growth predictions as a forward ranking problem},
author = {Karin A. van Garderen and Sebastian R. van der Voort and Maarten M. J. Wijnenga and Fatih Incekara and Georgios Kapsas and Renske Gahrmann and Ahmad Alafandi and Marion Smits and Stefan Klein},
journal= {arXiv preprint arXiv:2103.11651},
year = {2021}
}