Inter-software variability of Centiloid quantification in amyloid PET: A head-to-head comparison of five software platforms.

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.