Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease
| Journal Title: | European journal of nuclear medicine and molecular imaging 2018-10-31, Vol.46 (2), p.334-347 |
| Main Author: | De Carli, Fabrizio |
| Other Authors: | Nobili, Flavio , Pagani, Marco , Bauckneht, Matteo , Massa, Federico , Grazzini, Matteo , Jonsson, Cathrine , Peira, Enrico , Morbelli, Silvia , Arnaldi, Dario |
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 Electronic Article
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English |
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| Publisher: | Berlin/Heidelberg: Springer Berlin Heidelberg |
| ID: | ISSN: 1619-7070 |
| Link: | https://www.ncbi.nlm.nih.gov/pubmed/30382303 |
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| recordid: | cdi_proquest_miscellaneous_2127948918 |
| title: | Accuracy and generalization capability of an automatic method for the detection of typical brain hypometabolism in prodromal Alzheimer disease |
| format: | Article |
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| ispartof: | European journal of nuclear medicine and molecular imaging, 2018-10-31, Vol.46 (2), p.334-347 |
| description: | Purpose The aim of this study was to verify the reliability and generalizability of an automatic tool for the detection of Alzheimer-related hypometabolic pattern based on a Support-Vector-Machine (SVM) model analyzing 18 F-fluorodeoxyglucose (FDG) PET data. Methods The SVM model processed metabolic data from anatomical volumes of interest also considering interhemispheric asymmetries. It was trained on a homogeneous dataset from a memory clinic center and tested on an independent multicentric dataset drawn from the Alzheimer’s Disease Neuroimaging Initiative. Subjects were included in the study and classified based on a diagnosis confirmed after an adequate follow-up time. Results The accuracy of the discrimination between patients with Alzheimer Disease (AD), in either prodromal or dementia stage, and normal aging subjects was 95.8%, after cross-validation, in the training set. The accuracy of the same model in the testing set was 86.5%. The role of the two datasets was then reversed, and the accuracy was 89.8% in the multicentric training set and 88.0% in the monocentric testing set. The classification rate was also evaluated in different subgroups, including non-converter mild cognitive impairment (MCI) patients, subjects with MCI reverted to normal conditions and subjects with non-confirmed memory concern. The percent of pattern detections increased from 77% in early prodromal AD to 91% in AD dementia, while it was about 10% for healthy controls and non-AD patients. Conclusions The present findings show a good level of reproducibility and generalizability of a model for detecting the hypometabolic pattern in AD and confirm the accuracy of FDG-PET in Alzheimer disease. |
| language: | eng |
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| identifier: | ISSN: 1619-7070 |
| fulltext: | no_fulltext |
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| url: | Link |
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