Literature DB >> 22257044

Diagnostic accuracy of 18 F-FDG and 11 C-PIB-PET for prediction of short-term conversion to Alzheimer's disease in subjects with mild cognitive impairment.

S Zhang1, D Han, X Tan, J Feng, Y Guo, Y Ding.   

Abstract

In recent years, the role of PET imaging in the prediction of mild cognitive impairment (MCI) to Alzheimer's disease (AD) conversion has been the subject of many longitudinal studies. The purpose of this study was to perform a meta-analysis to estimate the diagnostic accuracy of (18) F-fluoro-2-deoxyglucose-positron emission tomography (FDG-PET) and (11) C-Pittsburgh Compound B-positron emission tomography (PIB-PET) for prediction of short-term conversion to AD in patients with MCI. The MEDLINE and EMBASE databases were systematically searched for relevant studies. Methodological quality of the included studies was assessed. Sensitivities and specificities of PET in individual studies were calculated and meta-analysis was undertaken with a random-effects model. A summary receiver operating characteristic (SROC) curve was constructed with the Moses-Shapiro-Littenberg method. Heterogeneity was tested, and the presence of publication bias was assessed. Potential sources for heterogeneity were explored by assessing whether or not certain covariates significantly influenced the relative diagnostic odds ratio (DOR). Pooled estimates of sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), DOR and the SROC curve of each PET imaging were determined. A total of 13 research studies (seven FDG-PET and six PIB-PET) met inclusion criteria and had sufficient data for statistical analysis. FDG-PET pooled estimates had 78.7% sensitivity (95% CI, 68.7-86.6%),74.0% specificity (95% CI, 67.0-80.3%), 18.1 LR+(95% CI, 7.3-45.0) and 0.32 LR-(95% CI, 0.16-0.61); and PIB-PET pooled estimates had 93.5% sensitivity (95%CI, 71.3-99.9%), 56.2% specificity (95% CI, 47.2-64.8%), 2.01 LR+ (95% CI, 1.57-2.58) and 0.17 LR-(95% CI, 0.08-0.36). Overall DOR was 17.3 (95% CI, 5.08-59.2) for FDG-PET and 12.8 (95% CI, 5.35-30.54) for PIB-PET. Area under the SROC curve was 0.88 ± 0.05 for FDG-PET and 0.85 ± 0.04 for PIB-PET. The data from FDG-PET research studies had high heterogeneity and funnel plot suggested a publication bias. The diagnostic accuracy determined for both FDG-PET and PIB-PET in this meta-analysis suggests that they are potentially valuable techniques for prediction of progression in patients with MCI. Both have their advantages and their combined use is a promising option for prediction purposes depending on availability and experience.
© 2012 Blackwell Publishing Ltd.

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Year:  2012        PMID: 22257044     DOI: 10.1111/j.1742-1241.2011.02845.x

Source DB:  PubMed          Journal:  Int J Clin Pract        ISSN: 1368-5031            Impact factor:   2.503


  34 in total

Review 1.  Clinical utility of FDG-PET for the clinical diagnosis in MCI.

Authors:  Javier Arbizu; Cristina Festari; Daniele Altomare; Zuzana Walker; Femke Bouwman; Jasmine Rivolta; Stefania Orini; Henryk Barthel; Federica Agosta; Alexander Drzezga; Peter Nestor; Marina Boccardi; Giovanni Battista Frisoni; Flavio Nobili
Journal:  Eur J Nucl Med Mol Imaging       Date:  2018-04-27       Impact factor: 9.236

2.  Combined Plasma and Cerebrospinal Fluid Signature for the Prediction of Midterm Progression From Mild Cognitive Impairment to Alzheimer Disease.

Authors:  Benoit Lehallier; Laurent Essioux; Javier Gayan; Roxana Alexandridis; Tania Nikolcheva; Tony Wyss-Coray; Markus Britschgi
Journal:  JAMA Neurol       Date:  2015-12-14       Impact factor: 18.302

Review 3.  Advances in CNS Imaging Agents: Focus on PET and SPECT Tracers in Experimental and Clinical Use.

Authors:  Noble George; Emily G Gean; Ayon Nandi; Boris Frolov; Eram Zaidi; Ho Lee; James R Brašić; Dean F Wong
Journal:  CNS Drugs       Date:  2015-04       Impact factor: 5.749

4.  Value of FDG-PET scans of non-demented patients in predicting rates of future cognitive and functional decline.

Authors:  Nare Torosyan; Kelsey Mason; Magnus Dahlbom; Daniel H S Silverman
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-03-22       Impact factor: 9.236

5.  Validation of olfactory deficit as a biomarker of Alzheimer disease.

Authors:  Matthew R Woodward; Chaitanya V Amrutkar; Harshit C Shah; Ralph H B Benedict; Sanjanaa Rajakrishnan; Rachelle S Doody; Li Yan; Kinga Szigeti
Journal:  Neurol Clin Pract       Date:  2017-02

6.  Disrupted topology of the resting state structural connectome in middle-aged APOE ε4 carriers.

Authors:  L E Korthauer; L Zhan; O Ajilore; A Leow; I Driscoll
Journal:  Neuroimage       Date:  2018-05-24       Impact factor: 6.556

7.  Neuroimaging and Dementia.

Authors:  Başar Bilgiç
Journal:  Noro Psikiyatr Ars       Date:  2018-03-19       Impact factor: 1.339

8.  Dual-phase amyloid PET: hitting two birds with one stone.

Authors:  Garibotto Valentina; Morbelli Silvia; Pagani Marco
Journal:  Eur J Nucl Med Mol Imaging       Date:  2016-04-23       Impact factor: 9.236

9.  Application of Concordance Probability Estimate to Predict Conversion from Mild Cognitive Impairment to Alzheimer's Disease.

Authors:  Xiaoxia Han; Yilong Zhang; Yongzhao Shao
Journal:  Biostat Epidemiol       Date:  2017-07-31

10.  Amyloid is linked to cognitive decline in patients with Parkinson disease without dementia.

Authors:  Stephen N Gomperts; Joseph J Locascio; Dorene Rentz; Andrea Santarlasci; Marta Marquie; Keith A Johnson; John H Growdon
Journal:  Neurology       Date:  2012-12-12       Impact factor: 9.910

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