Journal
FRONTIERS IN AGING NEUROSCIENCE
Volume 10, Issue -, Pages -Publisher
FRONTIERS MEDIA SA
DOI: 10.3389/fnagi.2018.00290
Keywords
alzheimer's disease; amnestic mild cognitive impairment; hippocampal subregions; radiomic features; support vector machine
Categories
Funding
- National Key Research and Development Program of China [2016YFC1305904]
- National Natural Science Foundation of China [81571062, 81471120, 61431012]
- Strategic Priority Research Program (B) of Chinese Academy of Sciences [XDBS01020200]
- Youth Innovation Promotion Association CAS [2014119]
- Primary Research & Development Plan of Shandong Province [2017GGX10112]
- Youth Cultivate Project for Medical Research in PLA [16QNP136]
- specific Healthcare Research Projects [13BJZ50]
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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 (P < 0.01, Bonferroni corrected). Among these features, 98 were significantly correlated with Mini-Mental State Examination (MMSE) scores in AD and aMCI subjects (P < 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.
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