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Comparison of Visual and Quantitative Florbetapir F 18 Positron Emission Tomography Analysis in Predicting Mild Cognitive Impairment Outcomes

Authors :
Schreiber, Stefanie
Landau, Susan M
Gamst, Anthony
Cellar, Janet S
Burns, Jeffrey M
Anderson, Heather S
Laubinger, Mary M
Bartzokis, George
Silverman, Daniel H S
Lu, Po H
Graff-Radford, Neill R
Parfitt, Francine
Johnson, Heather
Soares, Holly
Farlow, Martin
Herring, Scott
Hake, Ann M
van Dyck, Christopher H
MacAvoy, Martha G
Benincasa, Amanda L
Chertkow, Howard
Bergman, Howard
Hosein, Chris
Black, Sandra
Green, Robert C
Graham, Simon
Caldwell, Curtis
Hsiung, Ging-Yuek Robin
Feldman, Howard
Assaly, Michele
Kertesz, Andrew
Rogers, John
Trost, Dick
Bernick, Charles
Munic, Donna
Montine, Tom
Wu, Chuang-Kuo
Johnson, Nancy
Mesulam, Marsel
Sadowsky, Carl
Martinez, Walter
Villena, Teresa
Turner, Scott
Johnson, Kathleen B
Behan, Kelly E
Sperling, Reisa A
Thomas, Ronald G
Rentz, Dorene M
Johnson, Keith A
Rosen, Allyson
Tinklenberg, Jared
Ashford, Wes
Sabbagh, Marwan
Connor, Donald
Jacobson, Sandra
Killiany, Ronald
Norbash, Alexander
Donohue, Michael
Nair, Anil
Obisesan, Thomas O
Jayam-Trouth, Annapurni
Wang, Paul
Lerner, Alan
Hudson, Leon
Ogrocki, Paula
DeCarli, Charles
Fletcher, Evan
Carmichael, Owen
Walter, Sarah
Kittur, Smita
Mirje, Seema
Borrie, Michael
Lee, T-Y
Bartha, Rob
Johnson, Sterling
Asthana, Sanjay
Carlsson, Cynthia M
Potkin, Steven G
Preda, Adrian
Dale, Anders
Nguyen, Dana
Tariot, Pierre
Fleisher, Adam
Reeder, Stephanie
Bates, Vernice
Capote, Horacio
Rainka, Michelle
Hendin, Barry A
Scharre, Douglas W
Kataki, Maria
Bernstein, Matthew
Zimmerman, Earl A
Celmins, Dzintra
Brown, Alice D
Gandy, Sam
Marenberg, Marjorie E
Rovner, Barry W
Pearlson, Godfrey
Blank, Karen
Anderson, Karen
Saykin, Andrew J
Felmlee, Joel
Santulli, Robert B
Englert, Jessica
Williamson, Jeff D
Sink, Kaycee M
Watkins, Franklin
Ott, Brian R
Cohen, Ronald
Salloway, Stephen
Malloy, Paul
Fero, Allison
Fox, Nick
Correia, Stephen
Rosen, Howard J
Miller, Bruce L
Mintzer, Jacobo
Thompson, Paul
Schuff, Norbert
Alexander, Gene
Bandy, Dan
Chen, Kewei
Morris, John
Lee, Virginia M-Y
Korecka, Magdalena
Schreiber, Frank
Crawford, Karen
Neu, Scott
Harvey, Danielle
Kornak, John
Foroud, Tatiana M
Potkin, Steven
Shen, Li
Buckholtz, Neil
Kaye, Jeffrey
Jagust, William J
Dolen, Sara
Quinn, Joseph
Schneider, Lon
Pawluczyk, Sonia
Spann, Bryan M
Brewer, James
Vanderswag, Helen
Heidebrink, Judith L
Lord, Joanne L
Petersen, Ronald
Initiative, Alzheimer's Disease Neuroimaging
Johnson, Kris
Doody, Rachelle S
Villanueva-Meyer, Javier
Chowdhury, Munir
Stern, Yaakov
Honig, Lawrence S
Bell, Karen L
Morris, John C
Mintun, Mark A
Schneider, Stacy
Aisen, Paul
Marson, Daniel
Griffith, Randall
Clark, David
Grossman, Hillel
Tang, Cheuk
Marzloff, George
deToledo-Morrell, Leyla
Shah, Raj C
Duara, Ranjan
Varon, Daniel
Jack, Clifford R
Roberts, Peggy
Albert, Marilyn S
Pedroso, Julia
Toroney, Jaimie
Rusinek, Henry
de Leon, Mony J
De Santi, Susan M
Doraiswamy, P Murali
Petrella, Jeffrey R
Aiello, Marilyn
Toga, Arthur W
Clark, Christopher M
Pham, Cassie
Nunez, Jessica
Smith, Charles D
Given, Curtis A
Hardy, Peter
Lopez, Oscar L
Oakley, MaryAnn
Simpson, Donna M
Ismail, M Saleem
Beckett, Laurel
Brand, Connie
BA, Jennifer Richard
Mulnard, Ruth A
Thai, Gaby
Mc-Adams-Ortiz, Catherine
Diaz-Arrastia, Ramon
Martin-Cook, Kristen
DeVous, Michael
Levey, Allan I
Lah, James J
Source :
JAMA neurology 72(10), 1183 (2015). doi:10.1001/jamaneurol.2015.1633
Publication Year :
2015
Publisher :
American Medical Association, 2015.

Abstract

Importance The applicability of β-amyloid peptide (Aβ) positron emission tomography (PET) as a biomarker in clinical settings to aid in selection of individuals at preclinical and prodromal Alzheimer disease (AD) will depend on the practicality of PET image analysis. In this context, visual-based Aβ PET assessment seems to be the most feasible approach. Objectives To determine the agreement between visual and quantitative Aβ PET analysis and to assess the ability of both techniques to predict conversion from mild cognitive impairment (MCI) to AD. Design, Setting, and Participants A longitudinal study was conducted among the Alzheimer’s Disease Neuroimaging Initiative (ADNI) sites in the United States and Canada during a 1.6-year mean follow-up period. The study was performed from September 21, 2010, to August 11, 2014; data analysis was conducted from September 21, 2014, to May 26, 2015. Participants included 401 individuals with MCI receiving care at a specialty clinic (219 [54.6%] men; mean [SD] age, 71.6 [7.5] years; 16.2 [2.7] years of education). All participants were studied with florbetapir F 18 [ 18 F] PET. The standardized uptake value ratio (SUVR) positivity threshold was 1.11, and one reader rated all images, with a subset of 125 scans rated by a second reader. Main Outcomes and Measures Sensitivity and specificity of positive and negative [ 18 F] florbetapir PET categorization, which was estimated with cerebrospinal fluid Aβ1-42 as the reference standard. Risk for conversion to AD was assessed using Cox proportional hazards regression models. Results The frequency of Aβ positivity was 48.9% (196 patients; visual analysis), 55.1% (221 patients; SUVR), and 64.8% (166 patients; cerebrospinal fluid), yielding substantial agreement between visual and SUVR data (κ = 0.74) and between all methods (Fleiss κ = 0.71). For approximately 10% of the 401 participants in whom visual and SUVR data disagreed, interrater reliability was moderate (κ = 0.44), but it was very high if visual and quantitative results agreed (κ = 0.92). Visual analysis had a lower sensitivity (79% vs 85%) but higher specificity (96% vs 90%), respectively, compared with SUVR. The conversion rate was 15.2% within a mean of 1.6 years, and a positive [ 18 F] florbetapir baseline scan was associated with a 6.91-fold (SUVR) or 11.38-fold (visual) greater hazard for AD conversion, which changed only modestly after covariate adjustment for apolipoprotein e4, concurrent fludeoxyglucose F 18 PET scan, and baseline cognitive status. Conclusions and Relevance Visual and SUVR Aβ PET analysis may be equivalently used to determine Aβ status for individuals with MCI participating in clinical trials, and both approaches add significant value for clinical course prognostication.

Details

Language :
English
Database :
OpenAIRE
Journal :
JAMA neurology 72(10), 1183 (2015). doi:10.1001/jamaneurol.2015.1633
Accession number :
edsair.doi.dedup.....75628c333d646d6181e7ddca16936c9b
Full Text :
https://doi.org/10.1001/jamaneurol.2015.1633