Valentina Garibotto*, Sara Trombella, Luigi Antelmi, Paolo Bosco, Alberto Redolfi, Claire Tabouret-Viaud, Olivier Rager, Gabriel Gold, Panteleimon Giannakopoulos, Silvia Morbelli, Flavio Nobili, Robert Perneczky, Mira Didic, Eric Guedj, Alexander Drzezga, Rik Ossenkoppele, Bart Van Berckel, Osman Ratib and Giovanni B. Frisoni Pages 1186 - 1194 ( 9 )
<P>Objective: Automated voxel-based analysis methods are used to detect cortical hypometabolism typical of Alzheimer’s Disease (AD) on FDG-PET brain scans. We compared the accuracy of two clinically validated tools for their ability to identify those MCI subjects progressing to AD at followup, to evaluate the impact of the analysis method on FDG-PET diagnostic performance. </P><P> Methods: SPMGrid and BRASS (Hermes Medical Solutions, Stockholm, Sweden) were tested on 131 MCI and elderly healthy controls from the EADC PET dataset. The concordance between the tools was tested by correlating the quantitative parameters (z- and t-values), calculated by the two software tools, and by measuring the topographical overlap of the abnormal regions (Dice score). Three independent expert readers blindly assigned a diagnosis based on the two map sets. We used conversion to AD dementia as the gold standard. </P><P> Results: The t-map and z-map calculated with SPMGrid and BRASS, respectively, showed a good correlation (R > .50) for the majority of individual cases (128/131) and for the majority of selected regions of interest (ROIs) (98/116). The overlap of the hypometabolic patterns from the two tools was, however, poor (Dice score .36). The diagnostic performance was comparable, with BRASS showing significantly higher sensitivity (.82 versus .59) and SPMGrid showing higher specificity (.87 versus .52). </P><P> Conclusion: Despite similar diagnostic performance in predicting conversion to AD in MCI subjects, the two tools showed significant differences, and the maps provided by the tools showed limited overlap. These results underline the urgency for standardization across FDG-PET analysis methods for their use in clinical practice.</P>
FDG-PET, Alzheimer's disease, MCI, Automated analysis, tatistical parametric mapping, hypometabolic pattern.
Laboratory of Neuroimaging and Innovative Molecular Tracer, University of Geneva, Geneva, Laboratory of Neuroimaging and Innovative Molecular Tracer, University of Geneva, Geneva, University of Cote d'Azur, Inria Sophia Antipolis, Epione Research Project, Nice, IRCCS Fondazione Stella Maris, Viale del Tirreno 331, Pisa, Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Geneva, Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Geneva, University of Geneva, Geneva, University of Geneva, Geneva, Department of Nuclear Medicine, IRCCS AOU San Martino-IST, University of Genoa, Genoa, Department of Nuclear Medicine, IRCCS AOU San Martino-IST, University of Genoa, Genoa, Department of Psychiatry and Psychotherapy, Ludwig, Maximilians-Universitaet Muenchen, Munich, Aix-Marseille Universite, CNRS, Ecole Centrale Marseille, UMR 7249, Institut Fresnel, Marseille, Aix-Marseille Universite, CNRS, Ecole Centrale Marseille, UMR 7249, Institut Fresnel, Marseille, Department of Nuclear Medicine, Technische Universitaet, Munich, Department of Nuclear Medicine and PET Research, VU University Medical Center, Amsterdam, Department of Nuclear Medicine and PET Research, VU University Medical Center, Amsterdam, Division of Nuclear Medicine and Molecular Imaging, Diagnostic Department, University Hospitals of Geneva, Geneva, Laboratory of Neuroimaging of Aging, University of Geneva, Geneva