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European Journal of Nuclear Medicine and Molecular Imaging
Published in: European Journal of Nuclear Medicine and Molecular Imaging 3/2021

Open Access 01-03-2021 | Artificial Intelligence | Original Article

Classification of negative and positive 18F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network

Authors: Bart Marius de Vries, Sandeep S. V. Golla, Jarith Ebenau, Sander C. J. Verfaillie, Tessa Timmers, Fiona Heeman, Matthijs C. F. Cysouw, Bart N. M. van Berckel, Wiesje M. van der Flier, Maqsood Yaqub, Ronald Boellaard, Alzheimer’s Disease Neuroimaging Initiative

Published in: European Journal of Nuclear Medicine and Molecular Imaging | Issue 3/2021

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Abstract

Purpose

Visual reading of 18F-florbetapir positron emission tomography (PET) scans is used in the diagnostic process of patients with cognitive disorders for assessment of amyloid-ß (Aß) depositions. However, this can be time-consuming, and difficult in case of borderline amyloid pathology. Computer-aided pattern recognition can be helpful in this process but needs to be validated. The aim of this work was to develop, train, validate and test a convolutional neural network (CNN) for discriminating between Aß negative and positive 18F-florbetapir PET scans in patients with subjective cognitive decline (SCD).

Methods

18F-florbetapir PET images were acquired and visually assessed. The SCD cohort consisted of 133 patients from the SCIENCe cohort and 22 patients from the ADNI database. From the SCIENCe cohort, standardized uptake value ratio (SUVR) images were computed. From the ADNI database, SUVR images were extracted. 2D CNNs (axial, coronal and sagittal) were built to capture features of the scans. The SCIENCe scans were randomly divided into training and validation set (5-fold cross-validation), and the ADNI scans were used as test set. Performance was evaluated based on average accuracy, sensitivity and specificity from the cross-validation. Next, the best performing CNN was evaluated on the test set.

Results

The sagittal 2D-CNN classified the SCIENCe scans with the highest average accuracy of 99% ± 2 (SD), sensitivity of 97% ± 7 and specificity of 100%. The ADNI scans were classified with a 95% accuracy, 100% sensitivity and 92.3% specificity.

Conclusion

The 2D-CNN algorithm can classify Aß negative and positive 18F-florbetapir PET scans with high performance in SCD patients.
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Metadata
Title
Classification of negative and positive 18F-florbetapir brain PET studies in subjective cognitive decline patients using a convolutional neural network
Authors
Bart Marius de Vries
Sandeep S. V. Golla
Jarith Ebenau
Sander C. J. Verfaillie
Tessa Timmers
Fiona Heeman
Matthijs C. F. Cysouw
Bart N. M. van Berckel
Wiesje M. van der Flier
Maqsood Yaqub
Ronald Boellaard
Alzheimer’s Disease Neuroimaging Initiative
Publication date
01-03-2021
Publisher
Springer Berlin Heidelberg
Published in
European Journal of Nuclear Medicine and Molecular Imaging / Issue 3/2021
Print ISSN: 1619-7070
Electronic ISSN: 1619-7089
DOI
https://doi.org/10.1007/s00259-020-05006-3

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