title: Potential Clinical Value of Multiparametric PET in the Prediction of Alzheimer's Disease Progression.
abstract: To evaluate the potential clinical value of quantitative functional FDG PET and pathological amyloid-β PET with cerebrospinal fluid (CSF) biomarkers and clinical assessments in the prediction of Alzheimer's disease (AD) progression.
  
  
  
  We studied 82 subjects for up to 96 months (median = 84 months) in a longitudinal Alzheimer's Disease Neuroimaging Initiative (ADNI) project. All preprocessed PET images were spatially normalized to standard Montreal Neurologic Institute space. Regions of interest (ROI) were defined on MRI template, and standard uptake values ratios (SUVRs) to the cerebellum for FDG and amyloid-β PET were calculated. Predictive values of single and multiparametric PET biomarkers with and without clinical assessments and CSF biomarkers for AD progression were evaluated using receiver operating characteristic (ROC) analysis and logistic regression model.
  
  
  
  The posterior precuneus and cingulate SUVRs were identified for both FDG and amyloid-β PET in predicating progression in normal controls (NCs) and subjects with mild cognitive impairment (MCI). FDG parietal and lateral temporal SUVRs were suggested for monitoring NCs and MCI group progression, respectively. 18F-AV45 global cortex attained (78.6%, 74.5%, 75.4%) (sensitivity, specificity, accuracy) in predicting NC progression, which is comparable to the 11C-PiB global cortex SUVR's in predicting MCI to AD. A logistic regression model to combine FDG parietal and posterior precuneus SUVR and Alzheimer's Disease Assessment Scale-Cognitive (ADAS-Cog) Total Mod was identified in predicating NC progression with (80.0%, 94.9%, 93.9%) (sensitivity, specificity, accuracy). The selected model including FDG posterior cingulate SUVR, ADAS-Cog Total Mod, and Mini-Mental State Exam (MMSE) scores for predicating MCI to AD attained (96.4%, 81.2%, 83.6%) (sensitivity, specificity, accuracy). 11C-PiB medial temporal SUVR with MMSE significantly increased 11C-PiB PET AUC to 0.915 (p<0.05) in predicating MCI to AD with (77.8%, 90.4%, 88.5%) (sensitivity, specificity, accuracy).
  
  
  
  Quantitative FDG and 11C-PiB PET with clinical cognitive assessments significantly improved accuracy in the predication of AD progression.
authors.affiliation: Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.|The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: Xueqi
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.|The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: Yun
authors.initials: Y
authors.lastname: Zhou
authors.affiliation: Department of Nuclear Medicine, Peking University First Hospital, Beijing, China.
authors.forename: Rongfu
authors.initials: R
authors.lastname: Wang
authors.affiliation: University Hospital, Hamburg-Eppendorf, Hamburg, Germany.
authors.forename: Haoyin
authors.initials: H
authors.lastname: Cao
authors.affiliation: The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.
authors.forename: Savina
authors.initials: S
authors.lastname: Reid
authors.affiliation: The Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, United States of America.|Department of Nuclear Medicine, the First Affiliated Hospital of Xian Jiaotong University, Xi'an, Shaanxi, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Gao
authors.affiliation: Department of Computer Science and Engineering, Oakland University, Rochester, Michigan, United States of America.
authors.forename: Dong
authors.initials: D
authors.lastname: Han
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D000544:Alzheimer Disease / Q000000981:diagnostic imaging* / Q000209:etiology / Q000378:metabolism / Q000473:pathology*; D016229:Amyloid beta-Peptides / Q000378:metabolism; D015415:Biomarkers; D016208:Databases, Factual; D018450:Disease Progression; D005260:Female; D019788:Fluorodeoxyglucose F18; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D049268:Positron-Emission Tomography / Q000379:methods*; D011379:Prognosis; D012372:ROC Curve; D019275:Radiopharmaceuticals
chemical_list: D016229:Amyloid beta-Peptides; D015415:Biomarkers; D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18