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：基于图像的精准医疗旨在根据个体独特的影像特征个性化治疗决策，从而改善其临床结局。将不确定性估计纳入治疗推荐框架的机器学习方法会更安全可靠，但目前鲜有研究将其应用于精准医疗中的不确定性估计技术与验证指标。本文采用贝叶斯深度学习对多种治疗下的事实与反事实结局的后验分布进行估计，从而量化每种治疗方案的不确定性以及任意两种治疗间个体治疗效应（ITE）的不确定性。我们利用该模型训练并评估了多发性硬化症患者脑部MR影像的大规模多中心数据集，这些患者来自随机对照试验并接受过多种治疗，旨在预测未来新发及增大的T2病灶计数。我们分析了不确定性估计与实际误差的相关性，并针对反事实结局缺乏金标准的问题，展示了ITE预测中的不确定性如何与ITE误差的界限相关联。最后，我们论证了不确定性知识如何调整临床决策以改善个体患者及临床试验的结局。