Personalized medicine based on medical images, including predicting future individualized clinical disease progression and treatment response, would have an enormous impact on healthcare and drug development, particularly for diseases (e.g. multiple sclerosis (MS)) with long term, complex, heterogeneous evolutions and no cure. In this work, we present the first stochastic causal temporal framework to model the continuous temporal evolution of disease progression via Neural Stochastic Differential Equations (NSDE). The proposed causal inference model takes as input the patient's high dimensional images (MRI) and tabular data, and predicts both factual and counterfactual progression trajectories on different treatments in latent space. The NSDE permits the estimation of high-confidence personalized trajectories and treatment effects. Extensive experiments were performed on a large, multi-centre, proprietary dataset of patient 3D MRI and clinical data acquired during several randomized clinical trials for MS treatments. Our results present the first successful uncertainty-based causal Deep Learning (DL) model to: (a) accurately predict future patient MS disability evolution (e.g. EDSS) and treatment effects leveraging baseline MRI, and (b) permit the discovery of subgroups of patients for which the model has high confidence in their response to treatment even in clinical trials which did not reach their clinical endpoints.

翻译：基于医学影像的个性化医疗——包括预测未来个体化临床疾病进展与治疗反应——将对医疗保健与药物研发产生巨大影响，尤其对于具有长期、复杂、异质性演变且无法治愈的疾病（例如多发性硬化症）。本研究首次提出一种随机因果时序框架，通过神经随机微分方程建模疾病进展的连续时序演化。所提出的因果推断模型以患者的高维影像数据与表格数据作为输入，在潜在空间中预测不同治疗下的真实与反事实进展轨迹。神经随机微分方程支持对高置信度的个体化轨迹与治疗效应进行估计。我们在一个大型、多中心、专有的患者三维磁共振成像与临床数据集上进行了广泛实验，该数据采集自多项多发性硬化症治疗的随机临床试验。我们的研究首次实现了基于不确定性的因果深度学习模型，能够：（a）利用基线磁共振成像准确预测患者未来多发性硬化症残疾演变（如扩展残疾状态量表）与治疗效应；（b）即使在未达到临床终点的临床试验中，仍能识别出模型对其治疗反应具有高置信度的患者亚组。