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Eur J Nucl Med Mol Imaging. 2018 Jun;45(6):1052-1062. doi: 10.1007/s00259-017-3918-7. Epub 2017 Dec 23.

Extraction, selection and comparison of features for an effective automated computer-aided diagnosis of Parkinson's disease based on [123I]FP-CIT SPECT images.

Author information

1
Institute for Nuclear Sciences Applied to Health (ICNAS), and Institute for Biomedical Imaging and Life Sciences (IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal. francisco.oliveira@fundacaochampalimaud.pt.
2
Champalimaud Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal. francisco.oliveira@fundacaochampalimaud.pt.
3
HPP Medicina Molecular, Grupo Lenitudes Saúde, Porto, Portugal.
4
School of Health Sciences, University of Aveiro, Aveiro, Portugal.
5
Champalimaud Centre for the Unknown, Champalimaud Foundation, Lisbon, Portugal.
6
Institute for Nuclear Sciences Applied to Health (ICNAS), and Institute for Biomedical Imaging and Life Sciences (IBILI), Faculty of Medicine, University of Coimbra, Coimbra, Portugal.
7
Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Departamento de Engenharia Mecânica, Faculdade de Engenharia, Universidade do Porto, Porto, Portugal.

Abstract

PURPOSE:

This work aimed to assess the potential of a set of features extracted from [123I]FP-CIT SPECT brain images to be used in the computer-aided "in vivo" confirmation of dopaminergic degeneration and therefore to assist clinical decision to diagnose Parkinson's disease.

METHODS:

Seven features were computed from each brain hemisphere: five standard features related to uptake ratios on the striatum and two features related to the estimated volume and length of the striatal region with normal uptake. The features were tested on a dataset of 652 [123I]FP-CIT SPECT brain images from the Parkinson's Progression Markers Initiative. The discrimination capacities of each feature individually and groups of features were assessed using three different machine learning techniques: support vector machines (SVM), k-nearest neighbors and logistic regression.

RESULTS:

Cross-validation results based on SVM have shown that, individually, the features that generated the highest accuracies were the length of the striatal region (96.5%), the putaminal binding potential (95.4%) and the striatal binding potential (93.9%) with no statistically significant differences among them. The highest classification accuracy was obtained using all features simultaneously (accuracy 97.9%, sensitivity 98% and specificity 97.6%). Generally, slightly better results were obtained using the SVM with no statistically significant difference to the other classifiers for most of the features.

CONCLUSIONS:

The length of the striatal region uptake is clinically useful and highly valuable to confirm dopaminergic degeneration "in vivo" as an aid to the diagnosis of Parkinson's disease. It compares fairly well to the standard uptake ratio-based features, reaching, at least, similar accuracies and is easier to obtain automatically. Thus, we propose its day to day clinical use, jointly with the uptake ratio-based features, in the computer-aided diagnosis of dopaminergic degeneration in Parkinson's disease.

KEYWORDS:

Classification; DaTSCAN™; Dimensional-based features; PPMI database; Uptake ratios

PMID:
29275487
DOI:
10.1007/s00259-017-3918-7
[Indexed for MEDLINE]

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