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利用海马萎缩率和统计形状模型提高预测轻度认知障碍转化为阿尔茨海默病的能力。

Increasing power to predict mild cognitive impairment conversion to Alzheimer's disease using hippocampal atrophy rate and statistical shape models.

作者信息

Leung Kelvin K, Shen Kai-Kai, Barnes Josephine, Ridgway Gerard R, Clarkson Matthew J, Fripp Jurgen, Salvado Olivier, Meriaudeau Fabrice, Fox Nick C, Bourgeat Pierrick, Ourselin Sébastien

机构信息

Centre for Medical Image Computing, University College London, WC1E 6BT, UK.

出版信息

Med Image Comput Comput Assist Interv. 2010;13(Pt 2):125-32. doi: 10.1007/978-3-642-15745-5_16.

Abstract

Identifying mild cognitive impairment (MCI) subjects who will convert to clinical Alzheimer's disease (AD) is important for therapeutic decisions, patient counselling and clinical trials. Hippocampal volume and rate of atrophy predict clinical decline at the MCI stage and progression to AD. In this paper, we create p-maps from the differences in the shape of the hippocampus between 60 normal controls and 60 AD subjects using statistical shape models, and generate different regions of interest (ROI) by thresholding the p-maps at different significance levels. We demonstrate increased statistical power to classify 86 MCI converters and 128 MCI stable subjects using the hippocampal atrophy rates calculated by the boundary shift integral within these ROIs.

摘要

识别将转化为临床阿尔茨海默病(AD)的轻度认知障碍(MCI)患者对于治疗决策、患者咨询和临床试验至关重要。海马体积和萎缩率可预测MCI阶段的临床衰退以及向AD的进展。在本文中,我们使用统计形状模型根据60名正常对照者和60名AD患者海马形状的差异创建p值图,并通过在不同显著性水平下对p值图进行阈值处理来生成不同的感兴趣区域(ROI)。我们证明,使用这些ROI内通过边界位移积分计算的海马萎缩率对86名MCI转化者和128名MCI稳定患者进行分类时,统计功效有所提高。

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