摘要
脑内海马结构的形态学变化是阿尔茨海默病(AD)的影像学标记之一.海马的影像组学特征是否可以作为AD早期识别的生物标记以及是否可以预测轻度认知损害(MCI)的转化尚需进一步验证,并且海马影像组学特征是否有其神经生物学基础也需要进一步探索.本文的主要目的是研究海马的影像组学特征是否可以作为AD早期识别的影像学标记.为此,我们利用最常用的多变量分类器对AD(261例)和正常人(231例)进行独立中心识别的交叉验证,6个中心的平均准确率为88.21%,更进一步,利用完全独立的ADNI数据集(1228例被试)也验证了这一结果.通过对MCI人群的系统研究发现,部分重要海马影像组学特征与被试的生物学评分(主要包括APOE基因型、多基因风险分数、脑脊液的生物标记物Aβ和Tau,以及认知能力的变化等)具有显著的相关性.更为重要的是,通过5年左右的纵向跟踪研究发现影像组学特征的变化与认知水平的变化具有高度一致性.本研究结果表明海马的影像组学特征有希望用于早期识别AD,具有预测MCI病人是否会转化为AD的潜在能力.综上,研究结果将有可能应用于MCI和AD的早期辅助识别,具有重要的临床意义.
Hippocampal morphological change is one of the main hallmarks of Alzheimer’s disease(AD).However,whether hippocampal radiomic features are robust as predictors of progression from mild cognitive impairment(MCI)to AD dementia and whether these features provide any neurobiological foundation remains unclear.The primary aim of this study was to verify whether hippocampal radiomic features can serve as robust magnetic resonance imaging(MRI)markers for AD.Multivariate classifier-based support vector machine(SVM)analysis provided individual-level predictions for distinguishing AD patients(n=261)from normal controls(NCs;n=231)with an accuracy of 88.21%and intersite crossvalidation.Further analyses of a large,independent the Alzheimer’s Disease Neuroimaging Initiative(ADNI)dataset(n=1228)reinforced these findings.In MCI groups,a systemic analysis demonstrated that the identified features were significantly associated with clinical features(e.g.,apolipoprotein E(APOE)genotype,polygenic risk scores,cerebrospinal fluid(CSF)Ab,CSF Tau),and longitudinal changes in cognition ability;more importantly,the radiomic features had a consistently altered pattern with changes in the MMSE scores over 5 years of follow-up.These comprehensive results suggest that hippocampal radiomic features can serve as robust biomarkers for clinical application in AD/MCI,and further provide evidence for predicting whether an MCI subject would convert to AD based on the radiomics of the hippocampus.The results of this study are expected to have a substantial impact on the early diagnosis of AD/MCI.
基金
partially supported by the National Key Research and Development Program of China (2016YFC1305904)
the National Natural Science Foundation of China (81871438, 81901101, 61633018, 81571062, 81400890, 81871398)
the Strategic Priority Research Program (B) of the Chinese Academy of Sciences (XDB32020200)
the Beijing Municipal Science & Technology Commission (Z171100000117001, Z171100000117002)
the Primary Research & Development Plan of Shandong Province (2017GGX10112)
the Open Project Program of the National Laboratory of Pattern Recognition (NLPR) (201900021)
Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904)
DOD ADNI (Department of Defense award number W81XWH-12-2-0012)
funded by the National Institute on Aging
the National Institute of Biomedical Imaging and Bioengineering
generous contributions from Abb Vie, Alzheimer’s Association
Alzheimer’s Drug Discovery Foundation
The Canadian Institutes of Health Research provide funds to support ADNI clinical sites in Canada。
