ArticleTherapeutic advances in neurological disorders2021
Prediction of the progression from mild cognitive impairment to Alzheimer's disease using a radiomics-integrated model.
Article in Therapeutic advances in neurological disorders, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 39 citations in OpenAlex.
- 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
- Shape-Based Partially Linear Single-Index Cox Model for Alzheimer's Disease Conversion.Statistics in medicine · 2026Article
- Predicting cognitive frailty in older adults: an explainable machine learning model integrating radiomics and clinical features.BMC medical informatics and decision making · 2026Article
- How artificial intelligence is shaping neuropsychology: A focus on cognitive assessment of neurodegenerative disorders.Journal of neuropsychology · 2026Review
- Effect of -deficiency constitution on cognitive impairment in cerebral small vessel disease and its neuroimaging mechanism.Journal of traditional Chinese medicine = Chung i tsa chih ying wen pan · 2025Article
- Longitudinal structural MRI-based deep learning and radiomics features for predicting Alzheimer's disease progression.Alzheimer's research & therapy · 2025Article
- Advancements in deep learning for early diagnosis of Alzheimer's disease using multimodal neuroimaging: challenges and future directions.Frontiers in neuroinformatics · 2025Review
- Integrated cerebellar radiomic-network model for predicting mild cognitive impairment in Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Article
- Research progress in predicting the conversion from mild cognitive impairment to Alzheimer's disease via multimodal MRI and artificial intelligence.Frontiers in neurology · 2025Review
- Texture-based morphometry in relation to apolipoprotein ε4 genotype, ageing and sex in a midlife population.Human brain mapping · 2024Article
- Beyond Macrostructure: Is There a Role for Radiomics Analysis in Neuroimaging ?Magnetic resonance in medical sciences : MRMS : an official journal of Japan Society of Magnetic Resonance in Medicine · 2024Review
- Regional changes in brain metabolism during the progression of mild cognitive impairment: a longitudinal study based on radiomics.EJNMMI reports · 2024Article
- CT-based radiomics of machine-learning to screen high-risk individuals with kidney stones.Urolithiasis · 2024Article
- Intelligent prediction of Alzheimer's disease via improved multifeature squeeze-and-excitation-dilated residual network.Scientific reports · 2024Article
- Radiomic features of the hippocampal based on magnetic resonance imaging in the menopausal mouse model linked to neuronal damage and cognitive deficits.Brain imaging and behavior · 2024Article
- Screening Techniques for Drug Discovery in Alzheimer's Disease.ACS omega · 2024Review
- Machine learning-based prediction of mild cognitive impairment among individuals with normal cognitive function.Frontiers in neurology · 2024Article
- Investigating predictors of progression from mild cognitive impairment to Alzheimer's disease based on different time intervals.Age and ageing · 2023Article
- Multimodal multitask learning for predicting MCI to AD conversion using stacked polynomial attention network and adaptive exponential decay.Scientific reports · 2023Article
- Constructing a prognostic risk model for Alzheimer's disease based on ferroptosis.Frontiers in aging neuroscience · 2023Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveThis study aimed to build and validate a radiomics-integrated model with whole-brain magnetic resonance imaging (MRI) to predict the progression of mild cognitive impairment (MCI) to Alzheimer's disease (AD).
methods357 patients with MCI were selected from the ADNI database, which is an open-source database for AD with multicentre cooperation, of which 154 progressed to AD during the 48-month follow-up period. Subjects were divided into a training and test group. For each patient, the baseline T
resultsStepwise logistic regression analysis showed that the APOE4, clinical dementia rating, AD assessment scale, and radiomics signature were independent predictors of MCI progression to AD. The integrated model was constructed based on independent predictors using machine learning. The ROC curve showed that the accuracy of the model in the training and the test sets was 0.814 and 0.807, with a specificity of 0.671 and 0.738, and a sensitivity of 0.822 and 0.745, respectively. In addition, the model had the most significant diagnostic efficacy in predicting MCI progression to AD within 12 months, with an AUC of 0.814, sensitivity of 0.726, and specificity of 0.798.
conclusionThe integrated model based on whole-brain radiomics can accurately identify and predict the high-risk population of MCI patients who may progress to AD. Radiomics biomarkers are practical in the precursory stage of such disease.
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