Use of dynamic 18F-fluorodeoxyglucose positron emission tomography to investigate choroid plexus function in Alzheimer's disease

2016 ◽  
Vol 77 ◽  
pp. 62-68 ◽  
Author(s):  
Joël Daouk ◽  
Roger Bouzerar ◽  
Bader Chaarani ◽  
Jadwiga Zmudka ◽  
Marc-Etienne Meyer ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Positron Emission Tomography ◽  
Choroid Plexus ◽  
Emission Tomography ◽  
Fluorodeoxyglucose Positron Emission Tomography ◽  
18F Fluorodeoxyglucose ◽  
Positron Emission ◽  
Fluorodeoxyglucose Positron Emission

scholarly journals Deep-Learning Radiomics for Discrimination Conversion of Alzheimer's Disease in Patients With Mild Cognitive Impairment: A Study Based on 18F-FDG PET Imaging

2021 ◽  
Vol 13 ◽  
Author(s):  
Ping Zhou ◽  
Rong Zeng ◽  
Lun Yu ◽  
Yabo Feng ◽  
Chuxin Chen ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Positron Emission Tomography ◽  
Deep Learning ◽  
Cognitive Impairment ◽  
Emission Tomography ◽  
Fluorodeoxyglucose Positron Emission Tomography ◽  
Clinical Method ◽  
Positron Emission ◽  
Fluorodeoxyglucose Positron Emission

Objectives: Alzheimer's disease (AD) is the most prevalent neurodegenerative disorder and the most common form of dementia in the older people. Some types of mild cognitive impairment (MCI) are the clinical precursors of AD, while other MCI forms tend to remain stable over time and do not progress to AD. To discriminate MCI patients at risk of AD from stable MCI, we propose a novel deep-learning radiomics (DLR) model based on 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET) images and combine DLR features with clinical parameters (DLR+C) to improve diagnostic performance.Methods:18F-fluorodeoxyglucose positron emission tomography (PET) data from the Alzheimer's disease Neuroimaging Initiative database (ADNI) were collected, including 168 patients with MCI who converted to AD within 3 years and 187 patients with MCI without conversion within 3 years. These subjects were randomly partitioned into 90 % for the training/validation group and 10 % for the independent test group. The proposed DLR approach consists of three steps: base DL model pre-training, network features extraction, and integration of DLR+C, where a convolution network serves as a feature encoder, and a support vector machine (SVM) operated as the classifier. In comparative experiments, we compared our DLR+C method with four other methods: the standard uptake value ratio (SUVR) method, Radiomics-ROI method, Clinical method, and SUVR + Clinical method. To guarantee the robustness, 10-fold cross-validation was processed 100 times.Results: Under the DLR model, our proposed DLR+C was advantageous and yielded the best classification performance in the diagnosis of conversion with the accuracy, sensitivity, and specificity of 90.62 ± 1.16, 87.50 ± 0.00, and 93.39 ± 2.19%, respectively. In contrast, the respective accuracy of the other four methods reached 68.38 ± 1.27, 73.31 ± 6.93, 81.09 ± 1.97, and 85.35 ± 0.72 %. These results suggested the DLR approach could be used successfully in the prediction of conversion to AD, and that our proposed DLR-combined clinical information was effective.Conclusions: This study showed DLR+C could provide a novel and valuable method for the computer-assisted diagnosis of conversion to AD from MCI. This DLR+C method provided a quantitative biomarker which could predict conversion to AD in MCI patients.

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