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.

翻译：本研究提出了一种名为深度证据学习的不确定性量化框架在放疗剂量预测领域中的创新应用。基于开放知识驱动计划挑战数据集的医学影像，我们发现该模型经过网络训练后能够有效生成与预测误差存在相关性的不确定性估计值。这一实现需要对原始损失函数进行重构以确保稳定实施。研究发现：（i）认知不确定性与预测误差高度相关，其多个关联指标与蒙特卡洛丢弃法及深度集成方法相当或更优；（ii）相较于两种传统框架，深度证据学习中认知不确定性的中位误差随不确定性阈值的变化呈现更显著的线性关系，表明其对模型误差具有更均匀的校准灵敏度；（iii）相较于认知不确定性，随机不确定性在CT强度添加高斯噪声后呈现更显著的分布偏移，与其反映数据噪声的诠释相吻合。综合结果表明，深度证据学习是一种能为放疗剂量预测深度学习模型赋予统计稳健性的有效方法。为提升临床相关性，我们展示了如何利用该模型构建预测剂量-体积直方图的置信区间。