MRI联合海马、内嗅皮层体积鉴别阿尔茨海默病和轻度认知障碍

摘要

Objective To improve the diagnostic accuracy of Alzheimer's disease (AD) and mild cognitive impairment (MCI) by combining the magnetic resonance imaging (MRI)‐based volume of hippocampus and the entorhinal cortex . Methods Brain regions were segmented based on MRI images for 133 AD patients ,305 MCI patients and 190 normal con‐trols (NC) ,and then ,the volumes of the bilateral hippocampus and entorhinal cortex were attained for all participants . The Logistic regression model was then used to combine them into a new joint index .Receiver operating characteristic curve (ROC) was utilized to evaluate the classification ability of the joint index and four single indexes (i .e .,the volume of the bilateral hippocampus and entorhinal cortex) .Results The volume of the bilateral hippocampus and entorhinal cor‐tex were significantly different between AD and NC( P<0 .05) ,MCI( P<0 .05) .The volume of the bilateral hippocam‐pus and left entorhinal cortex were significantly different between NC and MCI ( P <0 .05) .The area under ROC(AUC) of the new joint index for the classification of NC‐MCI ,NC‐AD ,and MCI‐AD were 0 .902 ,0 .987 ,and 0 .974 .Corre‐spondingly ,the classification accuracies of the new joint index were 84 .3% ,95 .1% ,and 92 .3% for the point with the optimal threshold ,with sensitivities and specificities over 80% ,which were higher than those of four single indexes .Con‐clusion The combination of the volume of hippocampus and the entorhinal cortex could significantly improve the diagnos‐tic accuracy of MCI and AD patients .%目的：通过联合基于 MRI的联合海马体积和内嗅皮层体积，提高阿尔茨海默病（Alzheimer’s disease ，AD）和轻度认知障碍（mild cognitive impairment ，MCI）的诊断准确率。方法对133例 A D患者、305例 MCI患者及190例健康对照（normal control ，NC）的 MRI数据进行脑区分割，测量获得双侧海马和内嗅皮层体积，采用 Logistic回归模型合并双侧海马和内嗅皮层体积作为联合指标，利用受试者工作特性曲线（receiver operating characteristic curve ，ROC）评价联合指标和单一指标对三组被试的分类效果。结果 NC‐A D、MCI‐A D组间双侧海马和内嗅皮层体积均有显著差异（ P ＜0．05）；NC‐MCI组间双侧海马和左侧内嗅皮层体积均有显著差异（ P ＜0.05）。联合指标对于 NC‐MCI、NC‐AD、MCI‐AD的ROC曲线下面积（areaunder the curve ，AUC）分别为0．902、0．987、0．974，且最优阈值点的特异度和灵敏度均超过了80％，分类准确率分别为84．3％、95．1％、92．3％，均高于四种单一指标。结论利用双侧海马和内嗅皮层联合指标可提高 MCI和A D患者的诊断准确率。