18F-Florbetaben PET/CT to Assess Alzheimer's Disease: A new Analysis Method for Regional Amyloid Quantification.


Journal

Journal of neuroimaging : official journal of the American Society of Neuroimaging
ISSN: 1552-6569
Titre abrégé: J Neuroimaging
Pays: United States
ID NLM: 9102705

Informations de publication

Date de publication:
05 2019
Historique:
received: 12 10 2018
revised: 16 01 2019
accepted: 18 01 2019
pubmed: 5 2 2019
medline: 13 3 2020
entrez: 5 2 2019
Statut: ppublish

Résumé

While AD can be definitively confirmed by postmortem histopathologic examination, in vivo imaging may improve the clinician's ability to identify AD at the earliest stage. The aim of the study was to test the performance of amyloid PET using new processing imaging algorithm for more precise diagnosis of AD. Amyloid PET results using a new processing imaging algorithm (MRI-Less and AAL Atlas) were correlated with clinical, cognitive status, CSF analysis, and other imaging. The regional SUVR using the white matter of cerebellum as reference region and scores from clinical and cognitive tests were used to create ROC curves. Leave-one-out cross-validation was carried out to validate the results. Forty-four consecutive patients with clinical evidence of dementia, were retrospectively evaluated. Amyloid PET scan was positive in 26/44 patients with dementia. After integration with 18F-FDG PET, clinical data and CSF protein levels, 22 of them were classified as AD, the remaining 4 as vascular or frontotemporal dementia. Amyloid and FDG PET, CDR 1, CSF Tau, and p-tau levels showed the best true positive and true negative rates (amyloid PET: AUC = .85, sensitivity .91, specificity .79). A SUVR value of 1.006 in the inferior frontal cortex and of 1.03 in the precuneus region was the best cutoff SUVR value and showed a good correlation with the diagnosis of AD. Thirteen of 44 amyloid PET positive patients have been enrolled in clinical trials using antiamyloid approaches. Amyloid PET using SPM-normalized SUVR analysis showed high predictive power for the differential diagnosis of AD.

Sections du résumé

BACKGROUND AND PURPOSE
While AD can be definitively confirmed by postmortem histopathologic examination, in vivo imaging may improve the clinician's ability to identify AD at the earliest stage. The aim of the study was to test the performance of amyloid PET using new processing imaging algorithm for more precise diagnosis of AD.
METHODS
Amyloid PET results using a new processing imaging algorithm (MRI-Less and AAL Atlas) were correlated with clinical, cognitive status, CSF analysis, and other imaging. The regional SUVR using the white matter of cerebellum as reference region and scores from clinical and cognitive tests were used to create ROC curves. Leave-one-out cross-validation was carried out to validate the results.
RESULTS
Forty-four consecutive patients with clinical evidence of dementia, were retrospectively evaluated. Amyloid PET scan was positive in 26/44 patients with dementia. After integration with 18F-FDG PET, clinical data and CSF protein levels, 22 of them were classified as AD, the remaining 4 as vascular or frontotemporal dementia. Amyloid and FDG PET, CDR 1, CSF Tau, and p-tau levels showed the best true positive and true negative rates (amyloid PET: AUC = .85, sensitivity .91, specificity .79). A SUVR value of 1.006 in the inferior frontal cortex and of 1.03 in the precuneus region was the best cutoff SUVR value and showed a good correlation with the diagnosis of AD. Thirteen of 44 amyloid PET positive patients have been enrolled in clinical trials using antiamyloid approaches.
CONCLUSIONS
Amyloid PET using SPM-normalized SUVR analysis showed high predictive power for the differential diagnosis of AD.

Identifiants

pubmed: 30714241
doi: 10.1111/jon.12601
doi:

Substances chimiques

Aniline Compounds 0
Stilbenes 0
4-(N-methylamino)-4'-(2-(2-(2-fluoroethoxy)ethoxy)ethoxy)stilbene TLA7312TOI

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

IM

Pagination

383-393

Informations de copyright

© 2019 by the American Society of Neuroimaging.

Auteurs

Pierpaolo Alongi (P)

Department of Radiological Sciences, Nuclear Medicine Service, Fondazione Istituto G. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Davide Stefano Sardina (DS)

Department of Mathematics and Computer Science, University of Catania, Viale Andrea Doria 6, 95125, Catania, Italy.
Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy.

Rosalia Coppola (R)

U.O.C. Neurologia, Fondazione IstitutoG. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Salvatore Scalisi (S)

Department of Radiological Sciences, Nuclear Medicine Service, Fondazione Istituto G. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Valentina Puglisi (V)

U.O.C. Neurologia, Fondazione IstitutoG. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Annachiara Arnone (A)

University of Palermo, Palermo, Italy.

Giorgio Di Raimondo (GD)

U.O.C. Neurologia, Fondazione IstitutoG. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Elisabetta Munerati (E)

U.O.C. Neurologia, Fondazione IstitutoG. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Valerio Alaimo (V)

Department of Radiological Sciences, Unit of Radiology, Fondazione Istituto G. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Federico Midiri (F)

University of Palermo, Palermo, Italy.

Giorgio Russo (G)

Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy.

Alessandro Stefano (A)

Institute of Molecular Bioimaging and Physiology, National Research Council (IBFM-CNR), Cefalù, Italy.

Rosalba Giugno (R)

Department of Computer Science, University of Verona, Strada le Grazie 15, 37134, Verona, Italy.

Tommaso Piccoli (T)

Department of Biomedicine and Clinical Neuroscience, University of Palermo, Palermo, Italy.

Massimo Midiri (M)

Department of Radiological Sciences, Nuclear Medicine Service, Fondazione Istituto G. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Luigi M E Grimaldi (LME)

U.O.C. Neurologia, Fondazione IstitutoG. Giglio, Contrada Pietrapollastra-Pisciotto, 90015, Cefalù, Italy.

Articles similaires

[Redispensing of expensive oral anticancer medicines: a practical application].

Lisanne N van Merendonk, Kübra Akgöl, Bastiaan Nuijen
1.00
Humans Antineoplastic Agents Administration, Oral Drug Costs Counterfeit Drugs

Smoking Cessation and Incident Cardiovascular Disease.

Jun Hwan Cho, Seung Yong Shin, Hoseob Kim et al.
1.00
Humans Male Smoking Cessation Cardiovascular Diseases Female
Humans United States Aged Cross-Sectional Studies Medicare Part C
1.00
Humans Yoga Low Back Pain Female Male

Classifications MeSH