A data-driven regional amyloid PET score predicts cognitive decline beyond Centiloid
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Background
The Centiloid (CL) scale standardizes global amyloid PET quantification and is widely used to define amyloid positivity. As a global summary measure, however, CL may not fully reflect the regional distribution of amyloid deposition, which can carry additional prognostic information about the rate of cognitive decline.
Objective
To develop and externally validate a fixed, regional amyloid PET composite score that complements CL for predicting cognitive decline in Alzheimer’s disease.
Methods
The Regional Amyloid PET Score (RAPS) was derived from 82 FreeSurfer regions using machine learning with bootstrap stability selection to predict the rate of change in CDR–Sum of Boxes (CDR-SB) in 433 amyloid-positive ADNI [ 18 F]florbetapir participants. The fixed nine-region weights were applied without retraining in a cross-tracer ADNI [ 18 F]florbetaben subset ( N = 71; largely overlapping the discovery participants) and two external validation cohorts, NACC SCAN ( N = 1531; four tracers) and OASIS-3 ( N = 428).
Results
RAPS comprised nine regions. In ADNI, RAPS correlated more strongly with CDR-SB slope than CL and showed higher discrimination of rapid decliners (AUC 0.813 vs 0.713). Performance was directionally consistent across validation cohorts; in NACC SCAN, RAPS and CL independently predicted clinical progression. Cross-cohort meta-analysis of the three independent cohorts supported incremental discrimination beyond CL (pooled ΔAUC +0.066; I 2 = 0%).
Conclusions
RAPS, a fixed regional amyloid PET–derived score, may complement CL for prognostic stratification in Alzheimer’s disease research.
