Image-based precision medicine aims to personalize treatment decisions based on an individual's unique imaging features so as to improve their clinical outcome. Machine learning frameworks that integrate uncertainty estimation as part of their treatment recommendations would be safer and more reliable. However, little work has been done in adapting uncertainty estimation techniques and validation metrics for precision medicine. In this paper, we use Bayesian deep learning for estimating the posterior distribution over factual and counterfactual outcomes on several treatments. This allows for estimating the uncertainty for each treatment option and for the individual treatment effects (ITE) between any two treatments. We train and evaluate this model to predict future new and enlarging T2 lesion counts on a large, multi-center dataset of MR brain images of patients with multiple sclerosis, exposed to several treatments during randomized controlled trials. We evaluate the correlation of the uncertainty estimate with the factual error, and, given the lack of ground truth counterfactual outcomes, demonstrate how uncertainty for the ITE prediction relates to bounds on the ITE error. Lastly, we demonstrate how knowledge of uncertainty could modify clinical decision-making to improve individual patient and clinical trial outcomes.

翻译：图像基精准医疗旨在根据个体独特的影像特征个性化治疗决策，以改善其临床结局。将不确定性估计整合到治疗推荐中的机器学习框架将更安全可靠。然而，目前少有工作将不确定性估计技术与验证指标适配到精准医疗领域。本文采用贝叶斯深度学习估计多种治疗的事实与反事实结局的后验分布，从而实现对各治疗选项及任意两种治疗间个体治疗效果（ITE）的不确定性估计。我们在多发性硬化症患者脑部MR影像的大规模多中心数据集上训练并评估该模型——该数据集来自随机对照试验中暴露于多种治疗的患者，用于预测未来新发及扩大的T2病灶数量。我们评估了不确定性估计与事实误差的相关性，并在缺乏真实反事实结局数据的条件下，证明了ITE预测的不确定性如何与ITE误差的边界相关联。最后，我们展示了不确定性知识如何通过修正临床决策来改善个体患者及临床试验的结局。