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Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Aging Neurosci. | doi: 10.3389/fnagi.2018.00290

Radiomic features of hippocampal subregions in Alzheimer’s disease and amnestic mild cognitive impairment

 Feng Feng1, 2, 3,  Pan Wang1, 2, 4,  Kun Zhao5, 6, Bo Zhou1, 2, Hongxiang Yao2, 7, Qingqing Meng1, 2,  Lei Wang3, Zengqiang Zhang1, 2, Yanhui Ding6, Luning Wang1, 2, Ningyu An2, 7,  Xi Zhang1, 2* and  Yong Liu5, 8*
  • 1Department of Neurology, Nanlou Division, PLA General Hospital, China
  • 2Hainan Branch, PLA General Hospital, China
  • 3Department of Neurology, Rockets General Hospital of People’s Liberation Army, China
  • 4Department of Neurology, Tianjin Huanhu Hospital, China
  • 5Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, China
  • 6School of Information Science and Engineering, Shandong Normal University, China
  • 7Department of Radiology, PLA General Hospital, China
  • 8National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, China

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 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.

Keywords: Alzheimer’s disease, amnestic mild cognitive impairment, hippocampal subregions, radiomic features, Support vector machine

Received: 04 May 2018; Accepted: 03 Sep 2018.

Edited by:

Robert Perneczky, Ludwig-Maximilians-Universität München, Germany

Reviewed by:

Stefano Delli Pizzi, Università degli Studi G. d'Annunzio Chieti e Pescara, Italy
Feng Shi, Cedars-Sinai Medical Center, United States
Xianfeng Yang, Nanjing University of Science and Technology, China  

Copyright: © 2018 Feng, Wang, Zhao, Zhou, Yao, Meng, Wang, Zhang, Ding, Wang, An, Zhang and Liu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence:
Prof. Xi Zhang, PLA General Hospital, Department of Neurology, Nanlou Division, Beijing, China, zhangxi@301hospital.com.cn
Dr. Yong Liu, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China, yliu@nlpr.ia.ac.cn