Computer-aided diagnosis of Parkinson's disease based on [123I]FP-CIT SPECT binding potential images, using the voxels-as-features approach and support vector machines

• Published in: Journal of neural engineering Vol. 12; no. 2; pp. 26008 - 26017
• Main Authors: Oliveira, Francisco P M, Castelo-Branco, Miguel
• Format: Journal Article
• Language: English
• Published: England IOP Publishing 01.04.2015
• Subjects: Accuracy; automated image analysis; Binding; binding potential; Classification; Computation; DaTSCAN; Diagnosis; Dopamine Plasma Membrane Transport Proteins - metabolism; Female; Humans; Image Interpretation, Computer-Assisted - methods; Imaging, Three-Dimensional - methods; Male; Middle Aged; Molecular Imaging - methods; Parkinson Disease - diagnostic imaging; Parkinson Disease - metabolism; Parkinson's disease; Pattern Recognition, Automated - methods; Radiopharmaceuticals - pharmacokinetics; Reproducibility of Results; Sensitivity and Specificity; Support Vector Machine; Support vector machines; Tomography, Emission-Computed, Single-Photon - methods; Tropanes - pharmacokinetics
• DOI: 10.1088/1741-2560/12/2/026008
• ISSN: 1741-2560

Objective. The aim of the present study was to develop a fully-automated computational solution for computer-aided diagnosis in Parkinson syndrome based on [123I]FP-CIT single photon emission computed tomography (SPECT) images. Approach. A dataset of 654 [123I]FP-CIT SPECT brain images from the Parkinson's Progression Markers Initiative were used. Of these, 445 images were of patients with Parkinson's disease at an early stage and the remainder formed a control group. The images were pre-processed using automated template-based registration followed by the computation of the binding potential at a voxel level. Then, the binding potential images were used for classification, based on the voxel-as-feature approach and using the support vector machines paradigm. Main results. The obtained estimated classification accuracy was 97.86%, the sensitivity was 97.75% and the specificity 98.09%. Significance. The achieved classification accuracy was very high and, in fact, higher than accuracies found in previous studies reported in the literature. In addition, results were obtained on a large dataset of early Parkinson's disease subjects. In summation, the information provided by the developed computational solution potentially supports clinical decision-making in nuclear medicine, using important additional information beyond the commonly used uptake ratios and respective statistical comparisons. (ClinicalTrials.gov Identifier: NCT01141023)