Evaluation of semi-quantitative measures of 18 F-flutemetamol PET for the clinical diagnosis of Alzheimer’s disease

Quantitative Imaging in Medicine and Surgery

article

Ebba Gløersen Müller¹  Caroline Stokke³  Henning Langen Stokmo¹  Trine Holt Edwin⁵  Anne-Brita Knapskog⁶  Mona-Elisabeth Revheim¹ 

[1] Division of Radiology and Nuclear Medicine, Department of Nuclear Medicine , Oslo University Hospital;Institute of Clinical Medicine, Faculty of Medicine , University of Oslo;Division of Radiology and Nuclear Medicine, Department of Diagnostic Physics , Oslo University Hospital;Department of Physics , University of Oslo;Institute of Health and Society, Faculty of Medicine , University of Oslo;Department of Geriatric Medicine, The memory clinic , Oslo University Hospital

关键词: Flutemetamol;    quantification;    amyloid-beta;    positron emission tomography (PET);    Centiloid;   

DOI  :  10.21037/qims-21-188

学科分类：外科医学

来源: AME Publications

【 摘 要 】

Background: 18F-flutemetamol positron emission tomography (PET) is used to assess cortical amyloid-β burden in patients with cognitive impairment to support a clinical diagnosis. Visual classification is the most widely used method in clinical practice although semi-quantification is beneficial to obtain an objective and continuous measure of the Aβ burden. The aims were: first to evaluate the correspondence between standardized uptake value ratios (SUVRs) from three different software, Centiloids and visual classification, second to estimate thresholds for supporting visual classification and last to assess differences in semi-quantitative measures between clinical diagnoses. Methods: This observational study included 195 patients with cognitive impairment who underwent 18F-flutemetamol PET. PET images were semi-quantified with SyngoVia, CortexID suite, and PMOD. Receiver operating characteristics curves were used to compare visual classification with composite SUVR normalized to pons (SUVRpons) and cerebellar cortex (SUVRcer), and Centiloids. We explored correlations and differences between semi-quantitative measures as well as differences in SUVR between two clinical diagnosis groups: Alzheimer’s disease-group and non-Alzheimer’s disease-group. Results: 0.98). Thresholds for a visually positive PET was for SUVRcer: 1.87 (SyngoVia) and 1.64 (CortexID) and for SUVRpons: 0.54 (SyngoVia) and 0.55 (CortexID). The threshold on the Centiloid scale was 39.6 Centiloids. All semi-quantitative measures showed a very high correlation between different software and normalization methods. Composite SUVRcer was significantly different between SyngoVia and PMOD, SyngoVia and CortexID but not between PMOD and CortexID. Composite SUVRpons were significantly different between all three software. There were significant differences in the mean rank of SUVRpons, SUVRcer, and Centiloid between Alzheimer’s disease-group and non-Alzheimer’s disease-group. Conclusions: SUVR from different software performed equally well in discriminating visually positive and negative 18F-Flutemetamol PET images. Thresholds should be considered software-specific and cautiously be applied across software without preceding validation to categorize scans as positive or negative. SUVR and Centiloid may be used alongside a thorough clinical evaluation to support a clinical diagnosis.

【 授权许可】

All Rights reserved