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van Garderen, Karin A; Sebastian R van der Voort; Wijnenga, Maarten M J; Incekara, Fatih; Kapsas, Georgios; Gahrmann, Renske; Alafandi, Ahmad; Smits, Marion; Klein, Stefan
arXiv (Cornell University), 03/2021Paper, Journal Article
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.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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in: SICRIS
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