Assessment of Alzheimer’s Disease Based on Texture Analysis of the Entorhinal Cortex

• Published in: Frontiers in aging neuroscience Vol. 12; p. 176
• Main Authors: Leandrou, Stephanos, Lamnisos, Demetris, Mamais, Ioannis, Kyriacou, Panicos A., Pattichis, Constantinos S.
• Format: Journal Article
• Language: English
• Published: Lausanne Frontiers Research Foundation 02.07.2020; Frontiers Media S.A
• Subjects: Alzheimer's disease; Atrophy; Biomarkers; Brain research; Cognition & reasoning; Cognitive ability; Cortex (entorhinal); Dementia; entorhinal cortex; Magnetic resonance imaging; Medical imaging; Memory; Methods; mild cognitive impairment; Neurodegeneration; Neurodegenerative diseases; Neuroimaging; Neuroscience; Pathophysiology; Prediction models; texture; United States > US
• ISSN: 1663-4365; 1663-4365
• DOI: 10.3389/fnagi.2020.00176

Alzheimer's Disease (AD), brain Magnetic Resonance Imaging (MRI) biomarkers based on larger scale tissue neurodegeneration changes, such as atrophy, are currently widely used. Texture analysis, evaluates the statistical properties of the tissue image quantitatively, therefore, it could detect smaller scale changes of neurodegeneration. Entorhinal cortex is the first region affected and no study has investigated texture analysis on this region before. This study aims to differentiate AD patients from Normal Control (NC) and Mild Cognitive Impairment (MCI) subjects using entorhinal cortex texture features. Furthermore, it was evaluated whether texture has association to MCI beyond that of volume, to evaluate if may precede atrophy development. Texture features extracted from 194 NC, 200 MCI, 84 MCI who converted to AD (MCIc) and 130 AD subjects. Receiving operating characteristic curves, determined the performance of the various features in discriminating the groups, and a predictive model was used to predict conversion of MCIc subjects to AD. An area under curve of 0.872, 0.710, 0.730 and 0.764 was seen between NC vs AD, NC vs MCI, MCI vs MCIc and MCI vs AD subjects, respectively. Including entorhinal cortex volume improved the AUCs to 0.914, 0.740, 0.756 and 0.780, respectively. For the disease prediction, binary logistic regression was applied on five randomly selected test groups and achieved on average AUC's of 0.760 and 0.764 on the training and validation cohorts, respectively. Entorhinal cortex texture features were significantly different between the four groups and in many cases provided better results compared to other methods such as volumetry.