Fonteijn Hubert M, Clarkson Matthew J, Modat Marc, Barnes Josephine, Lehmann Manja, Ourselin Sebastien, Fox Nick C, Alexander Daniel C
Centre for Medical Image Computing, Department of Computer Science, University College London, UK.
Inf Process Med Imaging. 2011;22:748-59. doi: 10.1007/978-3-642-22092-0_61.
This study introduces a novel event-based model for disease progression. The model describes disease progression as a series of events. An event can consist of a significant change in symptoms or in tissue. We construct a forward model that relates heterogeneous measurements from a whole cohort of patients and controls to the event sequence and fit the model with a Bayesian estimation framework. The model does not rely on a priori classification of patients and therefore has the potential to describe disease progression in much greater detail than previous approaches. We demonstrate our model on serial T1 MRI data from a familial Alzheimer's disease cohort. We show progression of neuronal atrophy on a much finer level than previous studies, while confirming progression patterns from pathological studies, and integrate clinical events into the model.
本研究介绍了一种用于疾病进展的新型基于事件的模型。该模型将疾病进展描述为一系列事件。一个事件可以包括症状或组织的显著变化。我们构建了一个前向模型，将来自整个患者和对照组队列的异质测量与事件序列相关联，并使用贝叶斯估计框架对模型进行拟合。该模型不依赖于患者的先验分类，因此有可能比以前的方法更详细地描述疾病进展。我们在一个家族性阿尔茨海默病队列的连续T1 MRI数据上展示了我们的模型。我们在比以前的研究更精细的水平上显示了神经元萎缩的进展，同时证实了病理研究中的进展模式，并将临床事件整合到模型中。
