Alzheimer's disease: 3‐Dimensional MRI texture for prediction of conversion from mild cognitive impairment

Abstract

Introduction Currently, there are no tools that can accurately predict which patients with mild cognitive impairment (MCI) will progress to Alzheimer's disease (AD). Texture analysis uses image processing and statistical methods to identify patterns in voxel intensities that cannot be appreciated by visual inspection. Our main objective was to determine whether MRI texture could be used to predict conversion of MCI to AD. Methods A method of 3-dimensional, whole-brain texture analysis was used to compute texture features from T1-weighted MR images. To assess predictive value, texture changes were compared between MCI converters and nonconverters over a 3-year observation period. A predictive model using texture and clinical factors was used to predict conversion of patients with MCI to AD. This model was then tested on ten randomly selected test groups from the data set. Results Texture features were found to be significantly different between normal controls (n = 225), patients with MCI (n = 382), and patients with AD (n = 183). A subset of the patients with MCI were used to compare between MCI converters (n = 98) and nonconverters (n = 106). A composite model including texture features, APOE -ε4 genotype, Mini-Mental Status Examination score, sex, and hippocampal occupancy resulted in an area under curve of 0.905. Application of the composite model to ten randomly selected test groups (nonconverters = 26, converters = 24) predicted MCI conversion with a mean accuracy of 76.2%. Discussion Early texture changes are detected in patients with MCI who eventually progress to AD dementia. Therefore, whole-brain 3D texture analysis has the potential to predict progression of patients with MCI to AD.

Reliability of 3D texture analysis: A multicenter MRI study of the brain

“…However, voxelwise 3D TA instead computes the statistical differences in texture values. This technique has shown to be highly sensitive and specific in both Alzheimer's disease and ALS, supporting its potential as an imaging biomarker and diagnostic tool …”

“…TA has potential for clinical use, providing information on many neurological disorders. Indeed, it has been used to study intracranial brain tumors, epilepsy, multiple sclerosis, Alzheimer's disease, and amyotrophic lateral sclerosis (ALS) …”

“…2 TA has potential for clinical use, providing information on many neurological disorders. Indeed, it has been used to study intracranial brain tumors, 3,4 epilepsy, 5,6 multiple sclerosis, 1,7,8 Alzheimer's disease, [9][10][11][12][13] and amyotrophic lateral sclerosis (ALS). [14][15][16][17] Previous methods of using TA have been limited to 2D region-of-interest (ROI)-based approaches.…”

“…This technique has shown to be highly sensitive and specific in both Alzheimer's disease and ALS, supporting its potential as an imaging biomarker and diagnostic tool. [12][13][14][15][16][17] The study of neurological disorders has been facilitated by growing multicenter collaborative efforts, such as the Alzheimer's Disease Neuroimaging Initiative (ADNI). 18 Such consortia have been successful in increasing the pool of MRI data, and thus statistical power allowing for increased sensitivity to subtle changes in neuroimaging metrics.…”

Survey on Identification of Alzheimer Disease Using Magnetic Resonance Imaging (MRI) Images

“…Luk et al [16] had aimed at determining if the MRI may be used for converting the MCI to an AD. There was a 3-dimensional, analysis of the whole-brain texture method that was used for the computing of the texture features from the T1-weighted MR images.…”