Publicação
β-amyloid-stratified six-stage Alzheimer’s disease discrimination via multiband MRI histogram and GLCM features
| dc.contributor.author | Menezes, José | |
| dc.contributor.author | Barbosa, Maria Inês | |
| dc.contributor.author | Rodrigues, Pedro Miguel | |
| dc.date.accessioned | 2026-07-29T16:34:08Z | |
| dc.date.available | 2026-07-29T16:34:08Z | |
| dc.date.issued | 2026-11-01 | |
| dc.description.abstract | Accurate and early characterization of Alzheimer’s disease (AD) progression remains a major clinical challenge, particularly in biologically heterogeneous stages such as mild cognitive impairment (MCI). We propose an sMRI-based machine learning framework for multi-stage AD discrimination, encompassing six clinical groups (cognitively normal (CN, n=103), subjective memory complaints (SMC, n=54), early MCI (EMCI, n=151), MCI (n=229), late MCI (LMCI, n=117), and AD (n=114)), with explicit β-amyloid (Aβ) stratification of MCI subtypes (+/−). T1-weighted sMRI scans from 768 ADNI subjects were analyzed using histogram and GLCM texture features across multiple anatomical planes and multi-scale wavelet decompositions. The framework demonstrated robust performance across 36 pairwise comparisons under stratified 5-fold cross-validation with SMOTE and RUS, achieving AUC > 0.95 in well-separated tasks (e.g., AD vs EMCI−) and in SMC vs AD and SMC vs MCI+. More challenging “gray-zone” distinctions (e.g., EMCI− vs EMCI+) showed moderate performance (AUC ≈ 0.62–0.67), with RUS providing the most overall balanced results. Feature analysis revealed dominant contributions from GLCM descriptors, complemented by histogram features, with highest discriminability in Level-2 wavelet sub-bands (HL2 and LH2). Aβ stratification enabled a more biologically grounded interpretation of disease progression, improving discrimination in later stages while remaining challenging in early-stage comparisons. Overall, sMRI radiomics provides an interpretable framework for capturing stage-specific structural patterns in AD, highlighting the importance of biological stratification and robust validation. | eng |
| dc.identifier.doi | 10.1016/j.bspc.2026.111143 | |
| dc.identifier.eid | 105045682676 | |
| dc.identifier.other | ea5b025e-761f-4259-9d7d-a8854685781b | |
| dc.identifier.uri | http://hdl.handle.net/10400.14/58935 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Elsevier Ltd. | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.subject | Alzheimer’s disease | eng |
| dc.subject | Beta-amyloid | eng |
| dc.subject | Mild cognitive impairment | eng |
| dc.subject | sMRI | eng |
| dc.subject | Machine learning | eng |
| dc.subject | Discrimination | eng |
| dc.title | β-amyloid-stratified six-stage Alzheimer’s disease discrimination via multiband MRI histogram and GLCM features | |
| dc.type | research article | |
| dspace.entity.type | Publication | |
| oaire.citation.volume | 127 | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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