EJNMMI Research (Nov 2020)

Clinical impact of digital and conventional PET control databases for semi-quantitative analysis of brain 18F-FDG digital PET scans

  • Elise Mairal,
  • Matthieu Doyen,
  • Thérèse Rivasseau-Jonveaux,
  • Catherine Malaplate,
  • Eric Guedj,
  • Antoine Verger

DOI
https://doi.org/10.1186/s13550-020-00733-y
Journal volume & issue
Vol. 10, no. 1
pp. 1 – 10

Abstract

Read online

Abstract Purpose Digital PET cameras markedly improve sensitivity and spatial resolution of brain 18F-FDG PET images compared to conventional cameras. Our study aimed to assess whether specific control databases are required to improve the diagnostic performance of these recent advances. Methods We retrospectively selected two groups of subjects, twenty-seven Alzheimer's Disease (AD) patients and twenty-two healthy control (HC) subjects. All subjects underwent a brain 18F-FDG PET on a digital camera (Vereos, Philips®). These two group (AD and HC) are compared, using a Semi-Quantitative Analysis (SQA), to two age and sex matched controls acquired with a digital PET/CT (Vereos, Philips®) or a conventional PET/CT (Biograph 6, Siemens®) camera, at group and individual levels. Moreover, individual visual interpretation of SPM T-maps was provided for the positive diagnosis of AD by 3 experienced raters. Results At group level, SQA using digital controls detected more marked hypometabolic areas in AD (+ 116 cm3 at p < 0.001 uncorrected for the voxel, corrected for the cluster) than SQA using conventional controls. At the individual level, the accuracy of SQA for discriminating AD using digital controls was higher than SQA using conventional controls (86% vs. 80%, p < 0.01, at p < 0.005 uncorrected for the voxel, corrected for the cluster), with higher sensitivity (89% vs. 78%) and similar specificity (82% vs. 82%). These results were confirmed by visual analysis (accuracies of 84% and 82% for digital and conventional controls respectively, p = 0.01). Conclusion There is an urgent need to establish specific digital PET control databases for SQA of brain 18F-FDG PET images as such databases improve the accuracy of AD diagnosis.