  In this work, we present a novel application of an uncertainty-quantification
framework called Deep Evidential Learning in the domain of radiotherapy dose
prediction. Using medical images of the Open Knowledge-Based Planning Challenge
dataset, we found that this model can be effectively harnessed to yield
uncertainty estimates that inherited correlations with prediction errors upon
completion of network training. This was achieved only after reformulating the
original loss function for a stable implementation. We found that (i)epistemic
uncertainty was highly correlated with prediction errors, with various
association indices comparable or stronger than those for Monte-Carlo Dropout
and Deep Ensemble methods, (ii)the median error varied with uncertainty
threshold much more linearly for epistemic uncertainty in Deep Evidential
Learning relative to these other two conventional frameworks, indicative of a
more uniformly calibrated sensitivity to model errors, (iii)relative to
epistemic uncertainty, aleatoric uncertainty demonstrated a more significant
shift in its distribution in response to Gaussian noise added to CT intensity,
compatible with its interpretation as reflecting data noise. Collectively, our
results suggest that Deep Evidential Learning is a promising approach that can
endow deep-learning models in radiotherapy dose prediction with statistical
robustness. Towards enhancing its clinical relevance, we demonstrate how we can
use such a model to construct the predicted Dose-Volume-Histograms' confidence
intervals.
