ArticleNPJ dementia2025
Augmenting radiological assessment of imaging evident dementias with radiomic analysis.
Article in NPJ dementia, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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.
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Who cites it
1 citing paper in PubMed.
- Alzheimer's Disease Selectively Perturbs Age-Sensitive Brain Radiomic Features Across the Disease Continuum.bioRxiv : the preprint server for biology · 2026Article
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14 authors.
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Abstract
Accurate differential diagnosis of dementia is essential for guiding timely treatment, particularly as anti-amyloid therapies become more widely available and require precise patient characterization. Here, we developed a radiomics-based machine learning (ML) approach to enhance neuroimaging assessments in distinguishing Alzheimer's disease (AD) from other imaging-evident dementias (OIED). We retrospectively analyzed 1041 individuals from the National Alzheimer's Coordinating Center with confirmed dementia diagnoses and at least one T1 or T2/FLAIR MRI scan. Using FastSurfer and a Lesion Prediction Algorithm, we extracted volumetric and lesion features, which were then used to train ML models. Model performance was compared to the independent evaluations of seven fellowship-trained neuroradiologists. The classifier achieved an AUROC of 0.79 ± 0.01 for AD and 0.66 ± 0.03 for OIED, performing comparably to expert assessments. Interpretation using SHAP values showed strong alignment with imaging features known to align with AD or OIED, respectively. These findings highlight the potential of radiomics to augment neuroimaging workflows.
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