ArticleScientific reports2021
Predicting amyloid positivity in patients with mild cognitive impairment using a radiomics approach.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
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Who cites it
25 citing papers in PubMed, 37 citations in OpenAlex.
- Olfactory radiomics signatures link pTau217 to cognitive impairment in probable Alzheimer's disease: a multi-cohort machine learning study.Alzheimer's research & therapy · 2026Article
- Nano-radiomics of molecular MRI for early amyloid associated patterns in an Alzheimer's disease mouse model via an automatic pipeline.BMC medical imaging · 2026Article
- Predicting cognitive frailty in older adults: an explainable machine learning model integrating radiomics and clinical features.BMC medical informatics and decision making · 2026Article
- Predicting Brain Amyloid PET Positivity Using the Amyloid Beta Composite (ABC) Index in Patients With Cognitive Impairment.Dementia and neurocognitive disorders · 2026Article
- Generating MRI-Derived CSF Proxy-Markers to Predict and Visualize Alzheimer's Disease Progression.Human brain mapping · 2025Article
- Developments in MRI radiomics research for vascular cognitive impairment.Insights into imaging · 2025Review
- Machine learning models for dementia screening to classify brain amyloid positivity on positron emission tomography using blood markers and demographic characteristics: a retrospective observational study.Alzheimer's research & therapy · 2025Observational
- Korea-Registries to Overcome Dementia and Accelerate Dementia Research (K-ROAD): A Cohort for Dementia Research and Ethnic-Specific Insights.Dementia and neurocognitive disorders · 2024Article
- 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
- Comparison of deep learning architectures for predicting amyloid positivity in Alzheimer's disease, mild cognitive impairment, and healthy aging, from T1-weighted brain structural MRI.Frontiers in neuroscience · 2024Article
- Examining the diagnostic value of the mnemonic discrimination task for classification of cognitive status and amyloid-beta burden.Neuropsychologia · 2023Article
- Predicting positron emission tomography brain amyloid positivity using interpretable machine learning models with wearable sensor data and lifestyle factors.Alzheimer's research & therapy · 2023Observational
- MRI-based Deep Learning Assessment of Amyloid, Tau, and Neurodegeneration Biomarker Status across the Alzheimer Disease Spectrum.Radiology · 2023Article
- Radiomics and Artificial Intelligence for the Diagnosis and Monitoring of Alzheimer's Disease: A Systematic Review of Studies in the Field.Journal of clinical medicine · 2023Review
- Do radiomics or diffusion-tensor images provide additional information to predict brain amyloid-beta positivity?Scientific reports · 2023Article
- Prediction of Amyloid β-Positivity with both MRI Parameters and Cognitive Function Using Machine Learning.Journal of the Korean Society of Radiology · 2023Article
- Radiomics insight into the neurodegenerative "Frontiers in nuclear medicine · 2023Review
- Asymmetry of radiomics features in the white matter of patients with primary progressive aphasia.Frontiers in aging neuroscience · 2023Article
- Longitudinal changes in hippocampal texture from healthy aging to Alzheimer's disease.Brain communications · 2023Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Predicting amyloid positivity in patients with mild cognitive impairment (MCI) is crucial. In the present study, we predicted amyloid positivity with structural MRI using a radiomics approach. From MR images (including T1, T2 FLAIR, and DTI sequences) of 440 MCI patients, we extracted radiomics features composed of histogram and texture features. These features were used alone or in combination with baseline non-imaging predictors such as age, sex, and ApoE genotype to predict amyloid positivity. We used a regularized regression method for feature selection and prediction. The performance of the baseline non-imaging model was at a fair level (AUC = 0.71). Among single MR-sequence models, T1 and T2 FLAIR radiomics models also showed fair performances (AUC for test = 0.71-0.74, AUC for validation = 0.68-0.70) in predicting amyloid positivity. When T1 and T2 FLAIR radiomics features were combined, the AUC for test was 0.75 and AUC for validation was 0.72 (p vs. baseline model < 0.001). The model performed best when baseline features were combined with a T1 and T2 FLAIR radiomics model (AUC for test = 0.79, AUC for validation = 0.76), which was significantly better than those of the baseline model (p < 0.001) and the T1 + T2 FLAIR radiomics model (p < 0.001). In conclusion, radiomics features showed predictive value for amyloid positivity. It can be used in combination with other predictive features and possibly improve the prediction performance.
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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 Socratic graph.