Identifying shape-based biomarkers for diagnosis of Parkinson’s disease from ioflupane (123I) SPECT data

Abstract

Parkinson’s disease is a progressive, incurable neurodegenerative condition affecting movement which has been linked to poor quality of living and considerable socio-economic burdens. To date, treatment can at best slow down the degradation process. However, successful disease management is subject to early detection of the disease which, in turn, depends on the diagnostic process. Clinical investigation alone has proven insufficient in discriminating between early Parkinson’s disease and essential tremor. Functional neuroimaging circumvents this problem by visualising dopamine transporter concentrations in the brain, providing a differential even during the early stages of the disease. Yet the traditional visual assessment of SPECT data introduces subjectivity and susceptibility to variation whilst being impractical for monitoring and assessing disease progression. This work, presents a machine-learning approach to the assessment of three-dimensional SPECT data. The system extracts intensity and shape information from the data following binarisation which utilises an experimental approach towards the identification of an optimal threshold. The striatal binding ratio is calculated based on the three-dimensional data rather than two-dimensional clinical standard. The resulting semi-quantitative measure and the extracted intensity and shape information are collectively used as data features and are subjected to a support vector machine to classify between positive and negative cases of Parkinson’s disease. The classification system is reported to attain an average accuracy of 97%; with 96.6% sensitivity and 97.8% specificity. This shows an improvement over the clinical standard visual assessment which reportedly attained 94% sensitivity and 92% specificity.