ArticleFrontiers in cell and developmental biology2020
Radiomics Analysis of Magnetic Resonance Imaging Facilitates the Identification of Preclinical Alzheimer's Disease: An Exploratory Study.
Article in Frontiers in cell and developmental biology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03370744 (Prediction of Cognitive Decline by Neuroimaging Techniques and the Application in Diagnosis and Treatment of Preclinical AD), which is not on this map. Cited by 28 papers, 1 of them a synthesis that pooled it.
What it found
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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.
Prediction of Cognitive Decline by Neuroimaging Techniques and the Application in Diagnosis and Treatment of Preclinical AD (Sino Longitudinal Study on Cognitive Decline, SILCODE)
Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 51 citations in OpenAlex.
- Imaging biomarkers of glymphatic system dysfunction in Alzheimer's disease: a systematic review and meta-analysis with a focus on DTI-ALPS index.Frontiers in aging neuroscience · 2026Pooled it
- Biomarkers in preclinical and early Alzheimer's disease in China: a scoping review.The journal of prevention of Alzheimer's disease · 2026Article
- Radiomics-enhancedEuropean radiology experimental · 2026Article
- Enhancing interpretability of AI with radiomics-based deep neural network: proof of concept in the classification of Parkinsonian syndromes withEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Review
- Identification of texture MRI brain abnormalities on Fibromyalgia syndrome using interpretable machine learning models.Scientific reports · 2024Article
- A prediction model of dementia conversion for mild cognitive impairment by combining plasma pTau181 and structural imaging features.CNS neuroscience & therapeutics · 2024Article
- Integrating Demographics and Imaging Features for Various Stages of Dementia Classification: Feed Forward Neural Network Multi-Class Approach.Biomedicines · 2024Article
- A novel spatiotemporal graph convolutional network framework for functional connectivity biomarkers identification of Alzheimer's disease.Alzheimer's research & therapy · 2024Article
- Innovative Multivariable Model Combining MRI Radiomics and Plasma Indexes Predicts Alzheimer's Disease Conversion: Evidence from a 2-Cohort Longitudinal Study.Research (Washington, D.C.) · 2024Article
- Neuro-Vulnerability in Energy Metabolism Regulation: A Comprehensive Narrative Review.Nutrients · 2023Review
- Article
- An integrated neuroimaging-omics approach for the gut-brain communication pathways in Alzheimer's disease.Frontiers in aging neuroscience · 2023Review
- A cross-sectional study of explainable machine learning in Alzheimer's disease: diagnostic classification using MR radiomic features.Frontiers in aging neuroscience · 2023Article
- Identification of texture MRI brain abnormalities on first-episode psychosis and clinical high-risk subjects using explainable artificial intelligence.Translational psychiatry · 2022Article
- Diffusion Tensor Imaging Radiomics for Diagnosis of Parkinson's Disease.Brain sciences · 2022Article
- Using radiomics-based modelling to predict individual progression from mild cognitive impairment to Alzheimer's disease.European journal of nuclear medicine and molecular imaging · 2022Article
- β-Amyloid in blood neuronal-derived extracellular vesicles is elevated in cognitively normal adults at risk of Alzheimer's disease and predicts cerebral amyloidosis.Alzheimer's research & therapy · 2022Article
- Combination of gut microbiota and plasma amyloid-β as a potential index for identifying preclinical Alzheimer's disease: a cross-sectional analysis from the SILCODE study.Alzheimer's research & therapy · 2022Observational
- Using Deep Learning Radiomics to Distinguish Cognitively Normal Adults at Risk of Alzheimer's Disease From Normal Control: An Exploratory Study Based on Structural MRI.Frontiers in medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 2 countries.
Funding
Abstract
Diagnosing Alzheimer's disease (AD) in the preclinical stage offers opportunities for early intervention; however, there is currently a lack of convenient biomarkers to facilitate the diagnosis. Using radiomics analysis, we aimed to determine whether the features extracted from multiparametric magnetic resonance imaging (MRI) can be used as potential biomarkers. This study was part of the Sino Longitudinal Study on Cognitive Decline project (NCT03370744), a prospective cohort study. All participants were cognitively healthy at baseline. Cohort 1 (
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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.