A carregar...
Publicação
β-amyloid-stratified six-stage Alzheimer’s disease discrimination via multiband MRI histogram and GLCM features
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 3.57 MB | Adobe PDF |
Orientador(es)
Resumo(s)
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.
Descrição
Palavras-chave
Alzheimer’s disease Beta-amyloid Mild cognitive impairment sMRI Machine learning Discrimination
Contexto Educativo
Citação
Editora
Elsevier Ltd.