ArticleAlzheimer's & dementia (Amsterdam, Netherlands)2018
Alzheimer's disease: 3-Dimensional MRI texture for prediction of conversion from mild cognitive impairment.
Article in Alzheimer's & dementia (Amsterdam, Netherlands), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 2 of them syntheses that pooled it.
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Who cites it
24 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Limited generalizability and high risk of bias in multivariable models predicting conversion risk from mild cognitive impairment to dementia: A systematic review.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Pooled it
- Diagnostic performance of MRI radiomics for classification of Alzheimer's disease, mild cognitive impairment, and normal subjects: a systematic review and meta-analysis.Aging clinical and experimental research · 2023Pooled it
- Review
- Effects of a 5-week heart rate biofeedback randomized intervention on texture in the Alzheimer's Disease signature cortical region.Research square · 2026Article
- Explainable hierarchical machine-learning approaches for multimodal prediction of conversion from mild cognitive impairment to Alzheimer's disease.Physical and engineering sciences in medicine · 2025Article
- The application of radiomics in the diagnosis and evaluation of cognitive impairment related to neurological diseases.Frontiers in neuroscience · 2025Review
- Prediction models for cognitive impairment in middle-aged patients with cerebral small vessel disease.Frontiers in neurology · 2025Article
- Early Alzheimer's Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment.Diagnostics (Basel, Switzerland) · 2024Review
- Texture-based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population.Human brain mapping · 2024Article
- A multimodal machine learning model for predicting dementia conversion in Alzheimer's disease.Scientific reports · 2024Article
- Altered brain texture features in end-stage renal disease patients: a voxel-based 3D brain texture analysis study.Frontiers in neuroscience · 2024Article
- Automatic detection of mild cognitive impairment based on deep learning and radiomics of MR imaging.Frontiers in medicine · 2024Article
- MRI radiomics combined with machine learning for diagnosing mild cognitive impairment: a focus on the cerebellar gray and white matter.Frontiers in aging neuroscience · 2024Article
- Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease.Brain communications · 2023Article
- Distinct patterns of progressive gray and white matter degeneration in amyotrophic lateral sclerosis.Human brain mapping · 2022Article
- Alzheimer's Disease Diagnosis With Brain Structural MRI Using Multiview-Slice Attention and 3D Convolution Neural Network.Frontiers in aging neuroscience · 2022Article
- A boon to aged society: Early diagnosis of Alzheimer's disease-An opinion.Frontiers in public health · 2022Article
- Prediction of Cognitive Progression in Individuals with Mild Cognitive Impairment Using Radiomics as an Improvement of the ATN System: A Five-Year Follow-Up Study.Korean journal of radiology · 2022Article
- MRI Texture Analysis Reveals Brain Abnormalities in Medically Refractory Trigeminal Neuralgia.Frontiers in neurology · 2021Article
- Radiomic Features of the Hippocampus for Diagnosing Early-Onset and Late-Onset Alzheimer's Disease.Frontiers in aging neuroscience · 2021Article
Corrections and comments
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Authors and funding
9 authors.
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Abstract
introductionCurrently, 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.
methodsA 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.
resultsTexture 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, 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.
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