An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease
An event-based model for disease progression and its application in familial Alzheimer's disease and Huntington's disease
复制标题
DOI：
10.1016/j.neuroimage.2012.01.062
复制
发表时间：
2012-04-15
期刊：
影响因子：
5.7
通讯作者：
Alexander, Daniel C.
中科院分区：
文献类型：
--
作者：
Fonteijn, Hubert M.;Modat, Marc;Alexander, Daniel C.
了解神经系统疾病的进展对于准确和早期诊断和治疗计划至关重要。我们引入了一种新的疾病进展特征，将疾病描述为一系列事件，每个事件都包括患者状态的显著变化。我们提供了新的算法来学习整个患者队列的异质测量的事件排序，并使用来自家族性阿尔茨海默病和亨廷顿病队列的组合成像和临床数据进行演示。结果提供了这些疾病进展模式的新细节，同时证实了已知的特征，并对队列中进展的变异性提供了独特的见解。新模型和算法相对于先前进展模型的关键优势在于，它们不需要将患者先验地划分为临床阶段。该模型及其制定自然延伸到广泛的其他疾病和发展过程，并容纳横截面和纵向输入数据。(C)2012 Elsevier Inc. All rights reserved.
Understanding the progression of neurological diseases is vital for accurate and early diagnosis and treatment planning. We introduce a new characterization of disease progression, which describes the disease as a series of events, each comprising a significant change in patient state. We provide novel algorithms to learn the event ordering from heterogeneous measurements over a whole patient cohort and demonstrate using combined imaging and clinical data from familial Alzheimer's and Huntington's disease cohorts. Results provide new detail in the progression pattern of these diseases, while confirming known features, and give unique insight into the variability of progression over the cohort. The key advantage of the new model and algorithms over previous progression models is that they do not require a priori division of the patients into clinical stages. The model and its formulation extend naturally to a wide range of other diseases and developmental processes and accommodate cross-sectional and longitudinal input data. (C) 2012 Elsevier Inc. All rights reserved.