Comparison of SUVr metrics in PET with ¹⁸F-Flutemetamol using the SPM and CPET software packages

Authors

Keywords:

PET-CT, Alzheimer's Disease, Quantification Software, Flutemetamol, SPM

Abstract

Alzheimer's disease (AD) can be characterised by the deposition of β-amyloid (βA) plaques in the brain. Positron emission tomography with computed tomography (PET-CT) with 18F-Flutemetamol allows the anatomical-physiological identification and quantification of the βA plaques deposition through the Standard Uptake Value ratio (SUVr). These scans are typically quantified using paid software tools such as Comprehensive PET Analysis (CPET), economic motivations led to the development of the software Statistical Parametric Mapping (SPM) to replace CPET. The aim of this study was to compare the quantification of βA plaque density by both software programmes in patients with suspected AD, using metrics based on SUVr. A retrospective cross-sectional study was carried out with a non-probabilistic convenience sample of 30 18F-Flutemetamol PET-CT images in adult patients with suspected AD. All images were previously anonymized, ensuring participant confidentiality. As this study used fully anonymized secondary data, no additional ethical concerns were raised.

Statistical analysis was carried out using the SPSS software and the agreement between the software programmes ranged from poor (K=0.294) to very good (K=0.865).

In terms of diagnosis, the SPM showed a sensitivity of 82.35% and specificity of 81.25%, with an agreement rate of 90%, a false positive rate of 0% and a false negative rate of 18.75%. Currently, SPM should not replace CPET, as their agreement disparity can affect the diagnosis of patients. SPM could prove promising as a complementary or alternative tool to CPET if its methodology and accuracy improve.

References

1. CHMP. Guideline INN-flutemetamol (18F). European Medicines Agency. 2019;

2. Soria Lopez JA, González HM, Léger GC. Handbook of Clinical Neurology. Vol. 167, Handbook of Clinical Neurology. Elsevier; 2019. 231–255 p.

3. Hameed S, Fuh JL, Senanarong V, Ebenezer EGM, Looi I, Dominguez JC, et al. Role of Fluid Biomarkers and PET Imaging in Early Diagnosis and its Clinical Implication in the Management of Alzheimer’s Disease. J Alzheimers Dis Rep. 2020;4(1):21–37.

4. Smith NM, Ford JN, Haghdel A, Glodzik L, Li Y, D’Angelo D, et al. Statistical Parametric Mapping in Amyloid Positron Emission Tomography. Front Aging Neurosci. 2022 Apr 25;14:849932.

5. Consistent and reproducible quantification via AI tools [Internet]. GE Healthcare; 2021 [cited 2024 Mar 28]. Available from: https://pilot.cneuro.com/cpet/reports/pet?version=1.0.0

6. Jagust WJ, Mattay VS, Krainak DM, Wang SJ, Weidner LD, Hofling AA, Koo H, Hsieh P, Kuo PH, Farrar G, Marzella L. Quantitative Brain Amyloid PET. J Nucl Med. 2024 May 1;65(5):670-678. doi: 10.2967/jnumed.123.265766

7. Cattoretti M. Validation of an optimized SPM procedure for PET images analysis in neurodegenerative disease diagnosis in a clinical setting. [Varese]: Ospedale Di Circolo Fondazione Macchi; 2022

8. Presotto L, Ballarini T, Caminiti SP, Bettinardi V, Gianolli L, Perani D. Validation of 18 F FDG-PET Single-Subject Optimized SPM Procedure with Different PET Scanners. Neuroinformatics. 2017;15:151–63

9. Matsuda H, Soma T, Okita K, Shigemoto Y, Sato N. Development of software for measuring brain amyloid accumulation using 18F-florbetapir PET and calculating global Centiloid scale and regional Z-score values. Bischoff-Grethe A, editor. Vol. 13, Brain and Behavior. San Diego: John Wiley and Sons Ltd; 2023

10. Marôco J. Análise Estatística com o SPSS Statistics. 8th ed. Marôco J, editor. ReportNumber; 2021

11. Wang L, Kolobaric A, Aizenstein H, Lopresti B, Tudorascu D, Snitz B, et al. Identifying Sex specific Risk Architectures for Predicting Amyloid Deposition using Neural Networks. Neuroimage. 2023 Jul 7;275:120147

12. Pan F, Wang Y, Wang Y, Wang X, Guan Y, Xie F, et al. Sex and APOE genotype diferences in amyloid deposition and cognitive performance along the Alzheimer’s Continuum. Neurobiol Aging. 2023 Oct 1;130:84–92

13. Mosconi L, Berti V, Dyke J, Schelbaum E, Jett S, Loughlin L, et al. Menopause impacts human brain structure, connectivity, energy metabolism, and amyloid-beta deposition. Sci Rep. 2021 Dec 1;11(1):10867

14. Shang Y, Mishra A, Wang T, Wang Y, Desai M, Chen S, et al. Evidence in support of chromosomal sex influencing plasma based metabolome vs APOE genotype influencing brain met abolome profile in humanized APOE male and female mice. PLoS One. 2020 Jan 1;15(1)

Published

2026-03-06