U01FD007770

Automated Imaging Differentiation of Parkinsonism - Summary

Across the globe, the number of people diagnosed with Parkinsonism has increased considerably. From 1990 to 2015, the number of Parkinsonism diagnoses doubled, with over 6 million people currently carrying the diagnosis. Current estimates suggest that 12-14 million people will be diagnosed with Parkinsonism by 2040.

Parkinson's disease (PD), Multiple System Atrophy Parkinsonian Variant (MSAP), and Progressive Supranuclear Palsy (PSP), which are neurodegenerative forms of Parkinsonism, can be difficult to diagnose as they share motor and non-motor features and have an increased risk for dementia. Diagnostic accuracy in early PD (<5 years duration) is approximately 58%, and 54% of misdiagnosed patients have either MSA or PSP.

While the FDA has approved dopamine transporter imaging with DaTscan™ to help identify Parkinsonism, abnormal DaTscan imaging cannot distinguish between Parkinsonism forms that share dopaminergic deficiency. Thus, no clinically approved current diagnostic marker can distinguish among forms of Parkinsonism.

Correct diagnosis of Parkinsonism type is critical because the treatments, prognoses (often more rapid in atypical Parkinsonism), and pathologies of these diseases differ. Incorrect diagnoses result in patients receiving incorrect medications, deep brain stimulation surgeries performed in patients that do not have PD, diminished quality of life, and ineffective care.

As outlined in our accepted letter of intent to the FDA Biomarker Qualification Program, a promising approach to identify different forms of Parkinsonism is diffusion magnetic resonance imaging (DMRI). Our software method is based on free-water imaging, which is a method for analyzing DMRI data of tissue microstructure associated with inflammation and neurodegeneration.

We recently analyzed DMRI data from a retrospective multi-center cohort of 1002 participants collected with various acquisition protocols using 17 different MRI scanners across the world. Support Vector Machine (SVM) learning was conducted with an automated 5-fold cross-validation procedure in a training and validation cohort and then evaluated in an independent test cohort. In the independent test cohort, there was high area under the curve for distinguishing among PD, MSA, and PSP with AID-P across the MRI sites.

Two key issues raised in the feedback from our FDA Biomarker Letter of Intent included 1) examination of AID-P at different levels of disease severity; and 2) examination of AID-P on one MRI scanner vendor versus combining across MRI scanner vendor. In this U01 project, we will be examining these two analytical issues to further enhance the rigor for our final qualification plan. These key issues could have a significant impact on model prediction accuracy and thus impact patient care.

Automated Imaging Diagnostics

NOT APPLICABLE

93.103 - Food and Drug Administration Research

FDA Office of Acquisition and Grant Services (OAGS) [HHS - FDA]

Center for Drug Evaluation and Research (CDER) [HHS - FDA]

Florida United States

State-Wide

Drug Development Tools Research Grants (U01) Clinical Trial Optional (PAR-21-178)

Amendment Since initial award the End Date has been extended from 08/31/23 to 10/31/23.