标题： 时空条件扩散模型，用于在治疗条件下预测未来的多发性硬化症病灶掩码 显示英文标题

标题： Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments

摘要： 基于图像的个性化医疗有潜力改变医疗保健，特别是对于表现出异质性进展的疾病，如多发性硬化症（MS）。 在这项工作中，我们引入了第一个具有治疗意识的时空扩散模型，能够生成展示MS病灶演化的未来掩码。我们的体素空间方法结合了多模态患者数据，包括MRI和治疗信息，以预测未来时间点的新发和扩大T2（NET2）病灶掩码。 在来自复发缓解型MS随机临床试验的2131例患者3D MRI的多中心数据集上的大量实验表明，我们的生成模型能够准确预测接受六种不同治疗的患者的NET2病灶掩码。 此外，我们通过下游任务展示了我们的模型在现实临床应用中的潜力，例如未来病灶数量和位置估计、二进制病灶活动分类，以及为几种不同疗效的治疗生成反事实的未来NET2病灶掩码。 这项工作突显了因果关系的、基于图像的生成模型在推进MS数据驱动预后方面的潜力。 显示英文摘要

摘要： Image-based personalized medicine has the potential to transform healthcare, particularly for diseases that exhibit heterogeneous progression such as Multiple Sclerosis (MS). In this work, we introduce the first treatment-aware spatio-temporal diffusion model that is able to generate future masks demonstrating lesion evolution in MS. Our voxel-space approach incorporates multi-modal patient data, including MRI and treatment information, to forecast new and enlarging T2 (NET2) lesion masks at a future time point. Extensive experiments on a multi-centre dataset of 2131 patient 3D MRIs from randomized clinical trials for relapsing-remitting MS demonstrate that our generative model is able to accurately predict NET2 lesion masks for patients across six different treatments. Moreover, we demonstrate our model has the potential for real-world clinical applications through downstream tasks such as future lesion count and location estimation, binary lesion activity classification, and generating counterfactual future NET2 masks for several treatments with different efficacies. This work highlights the potential of causal, image-based generative models as powerful tools for advancing data-driven prognostics in MS.