ArticleNeuroradiology2026
Multiscale characterization and classification of Alzheimer's disease via integration of brain fingerprint radiomics and graph‑theoretical network metrics.
Article in Neuroradiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Not yet cited in PubMed.
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
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
- Erratum issued
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe rising prevalence of Alzheimer's disease (AD) associated with global population aging has become a critical health concern. Accurate and early diagnosis is essential for timely clinical intervention. Structural MRI-based radiomics and graph-theoretical modeling have recently provided new perspectives for identifying neurodegenerative changes.
objectiveThis study aimed to develop a unified framework integrating morphological brain fingerprinting and graph-theoretical network analysis to classify AD, mild cognitive impairment (MCI), and normal controls (NC), and to characterize the structural alterations across these distinct disease stages.
methodsHigh‑dimensional radiomic features were extracted from multiple AD‑related regions in structural MRI to construct individualized brain fingerprints. A graph structure was established using the inter‑regional correlation matrix, from which network metrics were derived to quantify topological connectivity, including node degree, clustering coefficient, and betweenness centrality. Support vector machine (SVM) and ensemble‑based classifiers were then employed to perform group classification and identify key imaging biomarkers.
resultsIn the AD vs NC classification, the model achieved the highest training accuracy (ACC = 0.9563), sensitivity (0.950), specificity (0.9625), and AUC = 0.995 (95% CI: 0.983-1.000); testing performance remained robust (ACC = 0.825, AUC = 0.935). For AD vs MCI and MCI vs NC, the training AUC were 0.972 and 0.952, while the testing AUC were 0.895 and 0.920, respectively. Feature mapping revealed a pattern of structural alteration, with the hippocampus, entorhinal cortex, and posterior cingulate showing significant abnormalities. Graph metrics indicated decreased integration efficiency and compensatory centrality increases within high-order cognitive networks.
conclusionThe proposed fingerprint-graph integration framework enables multilevel quantification of AD‑related structural reorganization, offering interpretable and reliable classification performance. It provides a promising foundation for early diagnosis, individualized assessment, and precision‑oriented neurodegenerative research.
Indexed as
Identifiers
42236609What 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.