Published 2021 | Version v1
Journal article

Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism

  • 1. Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, 11030, Manhasset, NY (United States)
  • 2. Larner College of Medicine, University of Vermont Medical Center, Burlington, VT (United States)
  • 3. Division of Movement Disorders, Columbia University Medical Center, New York, NY (United States)
  • 4. Department of Neurology, Boston University School of Medicine, Boston University, Boston, MA (United States)
  • 5. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA (United States)
  • 6. Division of Neuropathology, Columbia University Medical Center, New York, NY (United States)

Description

Up to 25% of patients diagnosed as idiopathic Parkinson's disease (IPD) have an atypical parkinsonian syndrome (APS). We had previously validated an automated image-based algorithm to discriminate between IPD, multiple system atrophy (MSA), and progressive supranuclear palsy (PSP). While the algorithm was accurate with respect to the final clinical diagnosis after long-term expert follow-up, its relationship to the initial referral diagnosis and to the neuropathological gold standard is not known. Patients with an uncertain diagnosis of parkinsonism were referred for 18F-fluorodeoxyglucose (FDG) PET to classify patients as IPD or as APS based on the automated algorithm. Patients were followed by a movement disorder specialist and subsequently underwent neuropathological examination. The image-based classification was compared to the neuropathological diagnosis in 15 patients with parkinsonism. At the time of referral to PET, the clinical impression was only 66.7% accurate. The algorithm correctly identified 80% of the cases as IPD or APS (p = 0.02) and 87.5% of the APS cases as MSA or PSP (p = 0.03). The final clinical diagnosis was 93.3% accurate (p < 0.001), but needed several years of expert follow-up. The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.

Availability note (English)

Available from: http://dx.doi.org/10.1007/s00259-021-05302-6

Additional details

Identifiers

Publishing Information

Journal Title
European Journal of Nuclear Medicine and Molecular Imaging
Journal Volume
48
Journal Issue
11
Journal Page Range
p. 3522-3529
ISSN
1619-7070
CODEN
EJNMA6

Optional Information

Notes
Advanced Image Analyses (Radiomics and Artificial Intelligence)