首个能预测多发性硬化治疗后病灶演变的时空生成模型
Spatio-Temporal Conditional Diffusion Models for Forecasting Future Multiple Sclerosis Lesion Masks Conditioned on Treatments
- 基于扩散模型融合影像与治疗信息，生成未来病灶分布
- 在2131例患者数据上准确预测6种治疗方案下的新发/扩大病灶
- 可辅助临床决策，支持治疗效果模拟与预后评估
基于图像的个性化医疗有望改变多发性硬化（MS）等异质性进展疾病的诊疗方式。本文首次提出一种治疗感知的时空扩散模型，可在体素空间中生成未来病变掩码，反映MS病灶演化过程。该模型融合多模态患者数据（包括MRI和治疗信息），用于预测未来时间点的新发与扩大T2（NET2）病灶掩码。在来自复发缓解型MS随机临床试验的2131例3D MRI多中心数据集上，实验表明该生成模型可准确预测不同治疗方案下的NET2病灶掩码。此外，通过下游任务如未来病灶数量与位置估计、病灶活动二分类以及生成不同疗效治疗方案的反事实未来掩码，验证了其在真实临床场景中的潜力。本研究展示了因果性、基于图像的生成模型在推动MS数据驱动预后分析方面的巨大前景。
原文摘要 · Abstract (English)
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.
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