Identifying New Biomarkers of Parkinson's From Routine Brain Imaging
Overview
- Phase
- Not Applicable
- Intervention
- Not specified
- Conditions
- Parkinson Disease
- Sponsor
- University of Plymouth
- Enrollment
- 20000
- Primary Endpoint
- The presence of novel putative biomarkers of future PD development as defined by a deep-learning method
- Last Updated
- 4 years ago
Overview
Brief Summary
The study will use routine computer tomography (CT), magnetic resonance spectroscopy (MRI) and nuclear medicine (NM) brain imaging data to produce new diagnostic tests for the onset of Parkinson's disease. This will enable hopefully earlier diagnosis than is currently possible. This will entail the analysis of anonymised CT/MRI/NM brain images collected prior to the point when these subjects were diagnosed with PD.
Detailed Description
We intend to use historical CT/MRI/nuclear medicine brain scans to identify novel imaging biomarkers of prodromal Parkinson's disease. The primary data source for the study will be MRI and CT brain scans, whilst nuclear medicine imaging brain imaging (DAT scans) will be used to validate models produced and provide a functional outcome measure of brain dopamine uptake. We shall utilise a an artificial intelligence approach to compare scans of PD cases with matched controls in order to identify these imaging biomarkers. A list of participants with a diagnosis will be compiled. This list, together with relevant clinical data, will be linked with historical CT/MRI/nuclear medicine scans carried out over the preceding years. A control group of matched non-PD scans will also be compiled. The dataset will be anonymised and a bespoke ML pipeline will be used to identify imaging featureswhich may be indicative of prodromal PD. This initial stage will be carried out at University Hospital Plymouth NHS Trust (UHPNT), the Royal Cornwall Hospital NHS Trust (RCHNT) and Cornwall Partnership NHS Trust (CPNT). If successful, findings will be validated in a larger sample of scans compiled from hospitals regionally and nationally.
Investigators
Stephen Mullin
Clinical Lecturer in Neurology
University of Plymouth
Eligibility Criteria
Inclusion Criteria
- •A clinical diagnosis of Parkinson's
Exclusion Criteria
- •No clinical diagnosis of Parkinson's
Outcomes
Primary Outcomes
The presence of novel putative biomarkers of future PD development as defined by a deep-learning method
Time Frame: 3 years