Head-to-Head Comparison among Semi-Quantification Tools of Brain FDG-PET to Aid the Diagnosis of Prodromal Alzheimer’s Disease¹

Article type: Research Article

Authors: Brugnolo, Andrea^{a; b; *} | De Carli, Fabrizio^c | Pagani, Marco^{d; e} | Morbelli, Slivia^{f; g} | Jonsson, Cathrine^h | Chincarini, Andreaⁱ | Frisoni, Giovanni B.^{j; k} | Galluzzi, Samantha^j | Perneczky, Robert^{l; m; n; o} | Drzezga, Alexander^p | van Berckel, Bart N.M.^q | Ossenkoppele, Rik^q | Didic, Mira^r | Guedj, Eric^s | Arnaldi, Dario^{a; t} | Massa, Federico^a | Grazzini, Matteo^a | Pardini, Matteo^{a; t} | Mecocci, Patrizia^u | Dottorini, Massimo E.^v | Bauckneht, Matteo^{f; g} | Sambuceti, Gianmario^{f; g} | Nobili, Flavio^{a; t}

Affiliations: [a] Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, and Mother-Child health (DINOGMI), University of Genoa, Italy | [b] Clinical Psychology Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy | [c] Institute of Bioimaging and Molecular Physiology, Consiglio Nazionale delle Ricerche (CNR), Genoa, Italy | [d] Institute of Cognitive Sciences and Technologies, Consiglio Nazionale delle Ricerche (CNR), Rome, Italy | [e] Department of Nuclear Medicine, Karolinska Hospital, Stockholm, Sweden | [f] Department of Health Sciences (DISSAL), University of Genoa, Italy | [g] Nuclear Medicine Unit, IRCCS Ospedale Policlinico San Martino, Genoa, Italy | [h] Medical Radiation Physics and Nuclear Medicine, Imaging and Physiology, Karolinska University Hospital, Stockholm, Sweden | [i] National Institute for Nuclear Physics (INFN), Genoa, Italy | [j] LENITEM Laboratory of Epidemiology and Neuroimaging, IRCCS S. Giovanni di Dio-FBF, Brescia, Italy | [k] University Hospitals and University of Geneva, Geneva, Switzerland | [l] Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Germany | [m] Department of Psychiatry and Psychotherapy, Technische Universität München, Munich, Germany | [n] German Center for Neurodegenerative Diseases (DZNE) Munich, Germany | [o] Neuroepidemiology and Ageing Research Unit, School of Public Health, Faculty of Medicine, The Imperial College London of Science, Technology and Medicine, London, UK | [p] Department of Nuclear Medicine, University Hospital of Cologne, Germany; previously at Department of Nuclear Medicine, Technische Universität, Munich, Germany | [q] Department of Nuclear Medicine & PET Research, VU University Medical Center, Amsterdam, The Netherlands | [r] APHM, CHU Timone, Service de Neurologie et Neuropsychologie, Aix-Marseille University, Marseille, France | [s] APHM, CHU Timone, Service de Médecine Nucléaire, CERIMED, Institut Fresnel, CNRS, Ecole Centrale Marseille, Aix-Marseille University, France | [t] Neurology Clinics, IRCCS Ospedale Policlinico San Martino, Genoa, Italy | [u] Section of Gerontology and Geriatrics, University of Perugia, Perugia, Italy | [v] Department of Diagnostic Imaging, Nuclear Medicine Unit, Perugia General Hospital, Perugia, Italy

Correspondence: [*] Correspondence to: Andrea Brugnolo, Clinical Neurology, Department of Neuroscience (DINOGMI), University of Genoa, Largo Daneo, 3, 16132 Genoa, Italy. Tel.: +39 010 3537778; Fax: +39 010 5556893; E-mail: [email protected].

Note: [1] This article received a correction notice (Erratum) with the reference: 10.3233/JAD-209003, available at https://content.iospress.com/articles/journal-of-alzheimers-disease/jad209003.

Abstract: 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.

Keywords: European Alzheimer Disease Consortium, FDG-PET, head-to-head comparison, prodromal Alzheimer’s disease, statistical parametric mapping, volumetric region of interest

DOI: 10.3233/JAD-181022

Journal: Journal of Alzheimer's Disease, vol. 68, no. 1, pp. 383-394, 2019