Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer's Disease

Andrea Brugnolo, Fabrizio de Carli, Marco Pagani, Slivia Morbelli, Cathrine Jonsson, Andrea Chincarini, Giovanni B. Frisoni, Samantha Galluzzi, Robert Perneczky, Alexander Drzezga, Bart N. M. van Berckel, Rik Ossenkoppele, Mira Didic, Eric Guedj, Dario Arnaldi, Federico Massa, Matteo Grazzini, Matteo Pardini, Patrizia Mecocci, Massimo E. DottoriniMatteo Bauckneht, Gianmario Sambuceti, Flavio Nobili

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11 Citations (Scopus)


Background: Several automatic tools have been implemented for semi-quantitative assessment of brain [ 18 ]F-FDG-PET. Objective: We aimed to head-to-head compare the diagnostic performance among three statistical parametric mapping (SPM)-based approaches, another voxel-based tool (i.e., PALZ), and a volumetric region of interest (VROI-SVM)-based approach, in distinguishing patients with prodromal Alzheimer's disease (pAD) from controls. Methods: Sixty-two pAD patients (MMSE score = 27.0±1.6) and one hundred-nine healthy subjects (CTR) (MMSE score = 29.2±1.2) were enrolled in five centers of the European Alzheimer's Disease Consortium. The three SPM-based methods, based on different rationales, included 1) a cluster identified through the correlation analysis between [ 18 ]F-FDG-PET and a verbal memory test (VROI-1), 2) a VROI derived from the comparison between pAD and CTR (VROI-2), and 3) visual analysis of individual maps obtained by the comparison between each subject and CTR (SPM-Maps). The VROI-SVM approach was based on 6 VROI plus 6 VROI asymmetry values derived from the pAD versus CTR comparison thanks to support vector machine (SVM). Results: The areas under the ROC curves between pAD and CTR were 0.84 for VROI-1, 0.83 for VROI-2, 0.79 for SPM maps, 0.87 for PALZ, and 0.95 for VROI-SVM. Pairwise comparisons of Youden index did not show statistically significant differences in diagnostic performance between VROI-1, VROI-2, SPM-Maps, and PALZ score whereas VROI-SVM performed significantly (p < 0.005) better than any of the other methods. Conclusion: The study confirms the good accuracy of [ 18 ]F-FDG-PET in discriminating healthy subjects from pAD and highlights that a non-linear, automatic VROI classifier based on SVM performs better than the voxel-based methods.

Original languageEnglish
Pages (from-to)383-394
Number of pages12
JournalJournal of Alzheimer's Disease
Issue number1
Publication statusPublished - 1 Jan 2019


  • European Alzheimer Disease Consortium
  • head-to-head comparison
  • prodromal Alzheimer's disease
  • statistical parametric mapping
  • volumetric region of interest

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