ArticleThe Journal of neuropsychiatry and clinical neurosciences2019
Brain MR Radiomics to Differentiate Cognitive Disorders.
Article in The Journal of neuropsychiatry and clinical neurosciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled 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
- Structural and microstructural changes in white and gray matter across the Alzheimer's disease continuum.Frontiers in aging neuroscience · 2025Review
- The application of radiomics in the diagnosis and evaluation of cognitive impairment related to neurological diseases.Frontiers in neuroscience · 2025Review
- Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics.EJNMMI reports · 2024Article
- Hippocampal sparing in whole-brain radiotherapy for brain metastases: controversy, technology and the future.Frontiers in oncology · 2024Review
- Explainable machine learning radiomics model for Primary Progressive Aphasia classification.Frontiers in systems neuroscience · 2024Article
- Development and validation of an automatic classification algorithm for the diagnosis of Alzheimer's disease using a high-performance interpretable deep learning network.European radiology · 2023Article
- Diffusion radiomics for subtyping and clustering in autism spectrum disorder: A preclinical study.Magnetic resonance imaging · 2023Article
- A new strategy for the early detection of alzheimer disease stages using multifractal geometry analysis based on K-Nearest Neighbor algorithm.Scientific reports · 2022Article
- Detection of Aspartylglucosaminuria Patients from Magnetic Resonance Images by a Machine-Learning-Based Approach.Brain sciences · 2022Article
- The Key Role of Magnetic Resonance Imaging in the Detection of Neurodegenerative Diseases-Associated Biomarkers: A Review.Molecular neurobiology · 2022Review
- Radiomics Model for Frontotemporal Dementia Diagnosis Using T1-Weighted MRI.Frontiers in neuroscience · 2022Article
- Weakly Supervised Skull Stripping of Magnetic Resonance Imaging of Brain Tumor Patients.Frontiers in neuroimaging · 2022Article
- Radiomics approach in the neurodegenerative brain.Aging clinical and experimental research · 2021Article
- Radiomics and Prostate MRI: Current Role and Future Applications.Journal of imaging · 2021Review
- Detection of β-amyloid positivity in Alzheimer's Disease Neuroimaging Initiative participants with demographics, cognition, MRI and plasma biomarkers.Brain communications · 2021Article
- Building the Precision Medicine for Mental Disorders via Radiomics/Machine Learning and Neuroimaging.Frontiers in neuroscience · 2021Article
- Quality Reporting of Radiomics Analysis in Mild Cognitive Impairment and Alzheimer's Disease: A Roadmap for Moving Forward.Korean journal of radiology · 2020Article
- From Medical Imaging to Radiomics: Role of Data Science for Advancing Precision Health.Journal of personalized medicine · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
objectiveSubtle and gradual changes occur in the brain years before cognitive impairment due to age-related neurodegenerative disorders. The authors examined the utility of hippocampal texture analysis and volumetric features extracted from brain magnetic resonance (MR) data to differentiate between three cognitive groups (cognitively normal individuals, individuals with mild cognitive impairment, and individuals with Alzheimer's disease) and neuropsychological scores on the Clinical Dementia Rating (CDR) scale.
methodsData from 173 unique patients with 3-T T
resultsThe results show promise for hippocampal texture analysis to distinguish between no impairment and early stages of impairment. Volumetric features were more successful at differentiating between no impairment and advanced stages of impairment.
conclusionsMR radiomics may be a promising tool to classify various cognitive groups.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.