通过海马体自动分割鉴别阿尔茨海默病、轻度认知障碍和正常衰老。
Discrimination between Alzheimer disease, mild cognitive impairment, and normal aging by using automated segmentation of the hippocampus.
作者信息
Colliot Olivier, Chételat Gaël, Chupin Marie, Desgranges Béatrice, Magnin Benoît, Benali Habib, Dubois Bruno, Garnero Line, Eustache Francis, Lehéricy Stéphane
机构信息
Cognitive Neuroscience and Brain Imaging Laboratory, Centre National de la Recherche Scientifique, UPR640-LENA, Université Pierre et Marie Curie-Paris 6, Hôpital de la Pitié-Salpêtrière, Paris, France.
出版信息
Radiology. 2008 Jul;248(1):194-201. doi: 10.1148/radiol.2481070876. Epub 2008 May 5.
PURPOSE
To prospectively evaluate the accuracy of automated hippocampal volumetry to help distinguish between patients with Alzheimer disease (AD), patients with mild cognitive impairment (MCI), and elderly controls, by using established criteria for patients with AD and MCI as the reference standard.
MATERIALS AND METHODS
The regional ethics committee approved the study and written informed consent was obtained from all participants. The study included 25 patients with AD (11 men, 14 women; mean age +/- standard deviation [SD], 73 years +/- 6; Mini-Mental State Examination (MMSE) score, 24.4 +/- 2.7), 24 patients with amnestic MCI (10 men, 14 women; mean age +/- SD, 74 years +/- 8; MMSE score, 27.2 +/- 1.4) and 25 elderly healthy controls (13 men, 12 women; mean age +/- SD, 64 years +/- 8). For each participant, the hippocampi were automatically segmented on three-dimensional T1-weighted magnetic resonance (MR) images with high spatial resolution. Segmentation was performed by using recently developed software that allows fast segmentation with minimal user input. Group differences in hippocampal volume were assessed by using Student t tests. To obtain robust estimates of P values, the correct classification rate, sensitivity, and specificity, bootstrap methods were used.
RESULTS
Significant hippocampal volume reductions were detected in all groups of patients (-32% in AD patients vs controls, P < .001; -19% in MCI patients vs controls, P < .001; and -15% in AD patients vs MCI patients, P < .01). Individual classification on the basis of hippocampal volume resulted in 84% correct classification (sensitivity, 84%; specificity, 84%) between AD patients and controls and 73% correct classification (sensitivity, 75%; specificity, 70%) between MCI patients and controls.
CONCLUSION
This automated method can serve as an alternative to manual tracing and may thus prove useful in assisting with the diagnosis of AD.
目的
以前瞻性方式评估自动海马体积测量的准确性，通过使用针对阿尔茨海默病（AD）和轻度认知障碍（MCI）患者的既定标准作为参考标准，来帮助区分AD患者、MCI患者和老年对照者。
材料与方法
地区伦理委员会批准了该研究，并获得了所有参与者的书面知情同意书。该研究纳入了25例AD患者（11例男性，14例女性；平均年龄±标准差[SD]，73岁±6岁；简易精神状态检查表（MMSE）评分，24.4±2.7），24例遗忘型MCI患者（10例男性，14例女性；平均年龄±SD，74岁±8岁；MMSE评分，27.2±1.4）以及25例老年健康对照者（13例男性，12例女性；平均年龄±SD，64岁±8岁）。对于每位参与者，在具有高空间分辨率的三维T1加权磁共振（MR）图像上自动分割海马体。分割使用最近开发的软件进行，该软件允许以最少的用户输入进行快速分割。通过使用学生t检验评估海马体积的组间差异。为了获得稳健的P值估计、正确分类率、敏感性和特异性，使用了自助法。
结果
在所有患者组中均检测到海马体积显著减小（AD患者与对照者相比减小32%，P <.001；MCI患者与对照者相比减小19%，P <.001；AD患者与MCI患者相比减小15%，P <.01）。基于海马体积的个体分类在AD患者与对照者之间的正确分类率为84%（敏感性，84%；特异性，84%），在MCI患者与对照者之间的正确分类率为73%（敏感性，75%；特异性，70%）。
结论
这种自动方法可作为手动追踪的替代方法，因此可能证明有助于AD的诊断。
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