Feng Feng, Wang Pan, Zhao Kun, Zhou Bo, Yao Hongxiang, Meng Qingqing, Wang Lei, Zhang Zengqiang, Ding Yanhui, Wang Luning, An Ningyu, Zhang Xi, Liu Yong
Department of Neurology, Nanlou Division, Chinese PLA General Hospital, National Clinical Research Center for Geriatric Diseases, Beijing, China.
Department of Neurology, The General Hospital of the PLA Rocket Force, Beijing, China.
Front Aging Neurosci. 2018 Sep 25;10:290. doi: 10.3389/fnagi.2018.00290. eCollection 2018.
Alzheimer's disease (AD) is characterized by progressive dementia, especially in episodic memory, and amnestic mild cognitive impairment (aMCI) is associated with a high risk of developing AD. Hippocampal atrophy/shape changes are believed to be the most robust magnetic resonance imaging (MRI) markers for AD and aMCI. Radiomics, a method of texture analysis, can quantitatively examine a large set of features and has previously been successfully applied to evaluate imaging biomarkers for AD. To test whether radiomic features in the hippocampus can be employed for early classification of AD and aMCI, 1692 features from the caudal and head parts of the bilateral hippocampus were extracted from 38 AD patients, 33 aMCI patients and 45 normal controls (NCs). One way analysis of variance (ANOVA) showed that 111 features exhibited statistically significant group differences ( < 0.01, Bonferroni corrected). Among these features, 98 were significantly correlated with Mini-Mental State Examination (MMSE) scores in AD and aMCI subjects ( < 0.01). The support vector machine (SVM) model demonstrated that radiomic features allowed us to distinguish AD from NC with an accuracy of 86.75% (specificity = 88.89% and sensitivity = 84.21%) and an area under curve (AUC) of 0.93. In conclusion, these findings provide evidence showing that radiomic features are beneficial in detecting early cognitive decline, and SVM classification analysis provides encouraging evidence for using hippocampal radiomic features as a potential biomarker for clinical applications in AD.
阿尔茨海默病（AD）的特征是进行性痴呆，尤其是情景记忆方面，而遗忘型轻度认知障碍（aMCI）与患AD的高风险相关。海马萎缩/形态改变被认为是AD和aMCI最可靠的磁共振成像（MRI）标志物。放射组学是一种纹理分析方法，可定量检测大量特征，此前已成功应用于评估AD的成像生物标志物。为了测试海马体的放射组学特征是否可用于AD和aMCI的早期分类，从38例AD患者、33例aMCI患者和45例正常对照（NC）中提取了双侧海马尾部和头部的1692个特征。单因素方差分析（ANOVA）显示，111个特征表现出具有统计学意义的组间差异（<0.01，经Bonferroni校正）。在这些特征中，98个与AD和aMCI受试者的简易精神状态检查表（MMSE）评分显著相关（<0.01）。支持向量机（SVM）模型表明，放射组学特征使我们能够以86.75%的准确率（特异性=88.89%，敏感性=84.21%）区分AD和NC，曲线下面积（AUC）为0.93。总之，这些发现提供了证据表明放射组学特征有助于检测早期认知衰退，并且SVM分类分析为将海马放射组学特征用作AD临床应用的潜在生物标志物提供了令人鼓舞的证据。
