1. Comparison of Visual and Quantitative Florbetapir F 18 Positron Emission Tomography Analysis in Predicting Mild Cognitive Impairment Outcomes
- Author
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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, and Lah, James J
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Male ,medicine.medical_specialty ,diagnostic imaging [Cognitive Dysfunction] ,metabolism [Amyloid beta-Peptides] ,Context (language use) ,Standardized uptake value ,Neuroimaging ,Alzheimer Disease ,Fluorodeoxyglucose F18 ,Predictive Value of Tests ,medicine ,Humans ,Cognitive Dysfunction ,therapy [Cognitive Dysfunction] ,ddc:610 ,Longitudinal Studies ,Psychiatry ,Aged ,Aged, 80 and over ,Amyloid beta-Peptides ,medicine.diagnostic_test ,therapy [Alzheimer Disease] ,business.industry ,methods [Positron-Emission Tomography] ,diagnosis [Alzheimer Disease] ,complications [Alzheimer Disease] ,Middle Aged ,medicine.disease ,Inter-rater reliability ,Treatment Outcome ,Positron emission tomography ,Positron-Emission Tomography ,Predictive value of tests ,Biomarker (medicine) ,Female ,Neurology (clinical) ,Alzheimer's disease ,Psychology ,Nuclear medicine ,business ,analysis [Biomarkers] ,complications [Cognitive Dysfunction] ,Biomarkers - 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.
- Published
- 2015
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