Alzheimer's & dementia (Amsterdam, Netherlands) · 2026
Inter-software variability of Centiloid quantification in amyloid PET: A head-to-head comparison of five software platforms.
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Zhang Jun, Burkett Brian J, Schwarz Christopher G, Dizdar Nur, Johnson Matthew P, Bilgin Cem, Kemp Bradley J, Durski Jolanta M, Lowe Val, Johnson Derek R
Аннотация
INTRODUCTION: Centiloid (CL) scaling standardizes amyloid positron emission tomography (PET) quantification across tracers and platforms; however, variability across software implementations may affect diagnostic classification. This study evaluated inter-software variability and diagnostic performance across five platforms using identical 1 8F-florbetapir datasets.
METHODS: Retrospectively, 192 patients undergoing 1 8F-florbetapir PET/computed tomography (CT) and magnetic resonance imaging (MRI) were analyzed. CL values were generated using four US Food and Drug Administration (FDA) -cleared platforms and an in-house Centiloid standard pipeline. Agreement was assessed using intraclass correlation coefficient, with bias and limits of agreement evaluated by linear modeling and Bland-Altman analysis. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and classification against visual interpretation.
RESULTS: Agreement was excellent (intraclass correlation coefficient [ICC] 0.969; 95%CI 0.961-0.975). Some platforms produced systematically higher CL values versus others slightly lower. LoA reached ± 30 CL. Diagnostic accuracy was high (area under the curve [AUC]: 0.949-0.975), with sensitivity 0.923-0.968 and specificity 0.667-0.806.
DISCUSSION: Despite excellent agreement, systematic differences persist and may affect classification near thresholds, supporting consistent use of a single processing pipeline.
METHODS: Retrospectively, 192 patients undergoing 1 8F-florbetapir PET/computed tomography (CT) and magnetic resonance imaging (MRI) were analyzed. CL values were generated using four US Food and Drug Administration (FDA) -cleared platforms and an in-house Centiloid standard pipeline. Agreement was assessed using intraclass correlation coefficient, with bias and limits of agreement evaluated by linear modeling and Bland-Altman analysis. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis and classification against visual interpretation.
RESULTS: Agreement was excellent (intraclass correlation coefficient [ICC] 0.969; 95%CI 0.961-0.975). Some platforms produced systematically higher CL values versus others slightly lower. LoA reached ± 30 CL. Diagnostic accuracy was high (area under the curve [AUC]: 0.949-0.975), with sensitivity 0.923-0.968 and specificity 0.667-0.806.
DISCUSSION: Despite excellent agreement, systematic differences persist and may affect classification near thresholds, supporting consistent use of a single processing pipeline.