Inter‐software variability of Centiloid quantification in amyloid PET: A head‐to‐head comparison of five software platforms
Jun Zhang, Brian J. Burkett, Christopher G. Schwarz, Nur Dizdar, Matthew P. Johnson, Cem Bilgin, Bradley J. Kemp, Jolanta M. Durski, Val Lowe, Derek R. JohnsonAbstract
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 8 F‐florbetapir datasets.
METHODS
Retrospectively, 192 patients undergoing 1 8 F‐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.