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：图像精准医学旨在基于个体独特的影像特征制定个性化治疗决策，以改善临床结局。将不确定性估计融入治疗推荐框架的机器学习系统将更安全可靠。然而，目前鲜有研究将不确定性估计技术与验证指标适配于精准医学场景。本文利用贝叶斯深度学习方法，对多种干预措施的事实结果与反事实结果的后验分布进行估计，从而能够量化各治疗方案的不确定性，以及任意两种治疗方案间的个体治疗效果（ITE）不确定性。我们在多发性硬化患者脑部MR影像大规模多中心数据集上训练并评估该模型——这些患者均参与随机对照试验并接受多种治疗，模型用于预测未来新增及扩大的T2病灶计数。我们评估了不确定性估计与事实误差的相关性，并在缺乏反事实结果真实标签的情况下，论证了ITE预测的不确定性与ITE误差边界的关联。最后，我们展示了不确定性知识如何优化临床决策，从而改善个体患者治疗结局与临床试验结果。