Literature DB >> 25589723

Prediction of Outcomes in Mild Cognitive Impairment by Using 18F-FDG-PET: A Multicenter Study.

Kengo Ito1, Hidenao Fukuyama, Michio Senda, Kazunari Ishii, Kiyoshi Maeda, Yasuji Yamamoto, Yasuomi Ouchi, Kenji Ishii, Ayumu Okumura, Ken Fujiwara, Takashi Kato, Yutaka Arahata, Yukihiko Washimi, Yoshio Mitsuyama, Kenichi Meguro, Mitsuru Ikeda.   

Abstract

BACKGROUND: 18F-FDG-PET is defined as a biomarker of neuronal injury according to the revised National Institute on Aging–Alzheimer’s Association criteria.
OBJECTIVE: The objective of this multicenter prospective cohort study was to examine the value of 18F-FDG-PET in predicting the development of Alzheimer’s disease (AD) in patients with mild cognitive impairment (MCI).
METHODS: In total, 114 patients with MCI at 9 participating institutions underwent clinical and neuropsychological examinations, MRI, and 18F-FDG-PET at baseline. The cases were visually classified into predefined dementia patterns by three experts. Anautomated analysis for 18F-FDG-PET was also performed to calculate the PET score. Subjects were followed periodically for 3 years, and progression to dementia was evaluated.
RESULTS: In 47% of the patients with MCI, progression of symptoms justified the clinical diagnosis of “probable AD”. The PET visual interpretation predicted conversion to AD during 3-year follow-up with an overall diagnostic accuracy of 68%. Overall diagnostic accuracy of the PET score was better than that of PET visual interpretation at all follow-up intervals, and the optimized PET score threshold revealed the best performance at the 2-year follow-up interval with an overall diagnostic accuracy of 83%,a sensitivity of 70%, and a specificity of 90%. Multivariate logistic regression analysis identified the PET score as the most significant predictive factor distinguishing AD converters from non-converters.
CONCLUSION: The PET score is the most statistically significant predictive factor for conversion from MCI to AD, and the diagnostic performance of the PET score is more promising for rapid converters over 2 years.

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Year:  2015        PMID: 25589723     DOI: 10.3233/JAD-141338

Source DB:  PubMed          Journal:  J Alzheimers Dis        ISSN: 1387-2877            Impact factor:   4.472


  21 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

Review 2.  A Cochrane review on brain [¹⁸F]FDG PET in dementia: limitations and future perspectives.

Authors:  Silvia Morbelli; Valentina Garibotto; Elsmarieke Van De Giessen; Javier Arbizu; Gaël Chételat; Alexander Drezgza; Swen Hesse; Adriaan A Lammertsma; Ian Law; Sabina Pappata'; Pierre Payoux; Marco Pagani
Journal:  Eur J Nucl Med Mol Imaging       Date:  2015-09       Impact factor: 9.236

3.  Early identification of MCI converting to AD: a FDG PET study.

Authors:  Marco Pagani; Flavio Nobili; Silvia Morbelli; Dario Arnaldi; Alessandro Giuliani; Johanna Öberg; Nicola Girtler; Andrea Brugnolo; Agnese Picco; Matteo Bauckneht; Roberta Piva; Andrea Chincarini; Gianmario Sambuceti; Cathrine Jonsson; Fabrizio De Carli
Journal:  Eur J Nucl Med Mol Imaging       Date:  2017-06-29       Impact factor: 9.236

4.  A concise and persistent feature to study brain resting-state network dynamics: Findings from the Alzheimer's Disease Neuroimaging Initiative.

Authors:  Liqun Kuang; Xie Han; Kewei Chen; Richard J Caselli; Eric M Reiman; Yalin Wang
Journal:  Hum Brain Mapp       Date:  2018-12-19       Impact factor: 5.038

5.  A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment.

Authors:  Min Wang; Zhuangzhi Yan; Shu-Yun Xiao; Chuantao Zuo; Jiehui Jiang
Journal:  Behav Neurol       Date:  2020-08-18       Impact factor: 3.342

Review 6.  An Algorithm for Preclinical Diagnosis of Alzheimer's Disease.

Authors:  Tapan K Khan
Journal:  Front Neurosci       Date:  2018-04-30       Impact factor: 4.677

7.  Visual Rating and Computer-Assisted Analysis of FDG PET in the Prediction of Conversion to Alzheimer's Disease in Mild Cognitive Impairment.

Authors:  Jae Myeong Kang; Jun-Young Lee; Yu Kyeong Kim; Bo Kyung Sohn; Min Soo Byun; Ji Eun Choi; Soo Kyung Son; Hyung-Jun Im; Jae-Hoon Lee; Young Hoon Ryu; Dong Young Lee
Journal:  Mol Diagn Ther       Date:  2018-08       Impact factor: 4.074

8.  Longer-Term Investigation of the Value of 18F-FDG-PET and Magnetic Resonance Imaging for Predicting the Conversion of Mild Cognitive Impairment to Alzheimer's Disease: A Multicenter Study.

Authors:  Yoshitaka Inui; Kengo Ito; Takashi Kato
Journal:  J Alzheimers Dis       Date:  2017       Impact factor: 4.472

9.  Early functional network alterations in asymptomatic elders at risk for Alzheimer's disease.

Authors:  Akinori Nakamura; Pablo Cuesta; Takashi Kato; Yutaka Arahata; Kaori Iwata; Misako Yamagishi; Izumi Kuratsubo; Kimiko Kato; Masahiko Bundo; Kersten Diers; Alberto Fernández; Fernando Maestú; Kengo Ito
Journal:  Sci Rep       Date:  2017-07-26       Impact factor: 4.379

10.  Prefrontal-Parietal White Matter Volumes in Healthy Elderlies Are Decreased in Proportion to the Degree of Cardiovascular Risk and Related to Inhibitory Control Deficits.

Authors:  Pedro P Santos; Paula S Da Silveira; Fabio L Souza-Duran; Jaqueline H Tamashiro-Duran; Márcia Scazufca; Paulo R Menezes; Claudia Da Costa Leite; Paulo A Lotufo; Homero Vallada; Maurício Wajngarten; Tânia C De Toledo Ferraz Alves; Patricia Rzezak; Geraldo F Busatto
Journal:  Front Psychol       Date:  2017-01-26
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