The necessary of ternary amyloid classification for clinical practice: An alternative to the binary amyloid definition

Abstract

Abstract Visual interpretation is considered the gold standard for amyloid scans in clinical practice. However, dichotomous classification of amyloid deposition by visual reading always results in bias due to rater experience. Therefore, there is a need for a more lenient and flexible amyloid‐equivocal classification in clinical practice. A total of 461 participants were included in this study. Amyloid and glucose positron‐emission tomography was performed, and neuropsychological tests were evaluated. A disease‐specific deep‐learning method was used to identify amyloid equivocality. Amyloid deposition, glucose metabolism, and cognitive performance were analyzed and compared among amyloid‐positive, amyloid‐negative, and amyloid‐equivocal groups. Clinically diagnosed Alzheimer's disease individuals and subjects with normal cognition were used to create amyloid biomarker cut points to support the definition of equivocal amyloid deposition. A total of 139 amyloid‐equivocal individuals were identified by deep learning methods. They displayed intermediate amyloid deposition between that of amyloid‐positive (standardized uptake value ratio [SUVr]: 1.25 ± 0.10 vs. 1.47 ± 0.20, p < 0.001) and amyloid‐negative (SUVr: 1.25 ± 0.10 vs. 1.18 ± 0.07, p < 0.001) individuals. No difference in glucose metabolism or cognitive performance was observed between amyloid negativity and equivocality. Furthermore, the SUVr for the whole cortex, the precuneus, and the frontal lobe served as auxiliary criteria supporting the diagnosis of equivocal amyloid deposition. We also established a guide to assist in the interpretation of amyloid equivocality by visual reading with auxiliary criteria including two cut points and deep learning methods.

Keywords

• amyloid deposition
• deep learning
• equivocal amyloid deposition
• glucose metabolism
• neurodegeneration