Alzheimer's Disease Classification Using Population-Referenced Brain Volumetric Percentiles

초록

Highlights What are the main findings? Population-referenced brain volumetric percentiles across 95 regions achieved excellent Alzheimer's disease classification with AUC values exceeding 0.960 on ADNI internal validation, ADNI test, and independent Korean external validation datasets. The minimal validation gap of 0.018 between ADNI and Korean cohorts demonstrates robust model generalization across different populations, scanner protocols, and demographic compositions without requiring dataset-specific retraining. What are the implications of the main findings? Percentile-based classification enables individual-level AD diagnosis without requiring longitudinal monitoring or age- and sex-matched control groups, addressing a major barrier to clinical translation of volumetric biomarkers. The dual-validation approach with external Korean cohort validation provides strong evidence that automated segmentation combined with population-referenced percentiles can serve as an accessible, cross-population neuroimaging tool for Alzheimer's disease assessment.Highlights What are the main findings? Population-referenced brain volumetric percentiles across 95 regions achieved excellent Alzheimer's disease classification with AUC values exceeding 0.960 on ADNI internal validation, ADNI test, and independent Korean external validation datasets. The minimal validation gap of 0.018 between ADNI and Korean cohorts demonstrates robust model generalization across different populations, scanner protocols, and demographic compositions without requiring dataset-specific retraining. What are the implications of the main findings? Percentile-based classification enables individual-level AD diagnosis without requiring longitudinal monitoring or age- and sex-matched control groups, addressing a major barrier to clinical translation of volumetric biomarkers. The dual-validation approach with external Korean cohort validation provides strong evidence that automated segmentation combined with population-referenced percentiles can serve as an accessible, cross-population neuroimaging tool for Alzheimer's disease assessment.Abstract Background/Objectives: Translating brain volumetric biomarkers to individual-level Alzheimer's disease (AD) diagnosis remains challenging due to difficulty interpreting raw volumes without longitudinal monitoring or matched controls. We tested a classification model using population-referenced volumetric percentiles to distinguish AD from cognitively normal (CN) subjects and evaluated its generalization across independent cohorts. Methods: Brain volumes from 95 regions were extracted using an automated segmentation pipeline and converted to age and sex adjusted percentiles using a reference population (N = 1833). A logistic regression classifier was trained on ADNI subjects (N = 873; AD = 183, CN = 690) split into training (60%), validation (20%), and test (20%) sets. The model was evaluated on two independent validation datasets: the held-out ADNI validation set and an external Korean cohort (N = 72; AD = 36, CN = 36) acquired with different scanner protocols and demographic characteristics. Results: The model achieved excellent discrimination across all evaluation sets: ADNI validation (AUC = 0.963, accuracy = 90.3%), ADNI test (AUC = 0.960, accuracy = 89.7%), and Korean external validation (AUC = 0.981, accuracy = 87.5%). The minimal validation gap (0.018) demonstrated robust generalization. Positive coefficients for ventricular regions reflected AD-associated atrophy patterns, while negative coefficients for medial temporal structures indicated their contribution within multivariate patterns distinguishing AD from normal aging. Conclusions: Population-referenced brain volumetric percentiles enable accurate AD classification with robust generalization across populations and scanner protocols. By contextualizing individual brain structure relative to normative populations while accounting for age and sex, this approach demonstrates potential for clinical translation as an accessible neuroimaging-based diagnostic tool.

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