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Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism

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European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

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.

Methods

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.

Results

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.

Conclusion

The image-based classifications agreed well with autopsy and can help to improve diagnostic accuracy during the period of clinical uncertainty.

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Acknowledgements

The authors thank Vicky Brandt and Dr. Martin Niethammer for helpful discussions and suggestions. We also thank Rosie Persaud for her invaluable assistance with coordinating data collection.

Availability of data and material

Deidentified data will be made available on reasonable request from interested investigators for the purpose of replicating results.

Funding

Aspects of this work were supported by the National Institute of Neurological Disorders and Stroke (P50 NS 071675 (Morris K. Udall Center of Excellence for Parkinson’s Disease Research at The Feinstein Institute for Medical Research) to D.E.). K.A.S. is supported by the Leopoldina Fellowship Program of the German National Academy of Sciences Leopoldina (LDS 2016-08) and the Postdoctoral Fellowship Grant No. PF-FBS-1929 from the Parkinson’s Foundation.

Author information

Author notes

  1. Katharina A. Schindlbeck and Deepak K. Gupta contributed equally to this work.

Authors and Affiliations

  1. Center for Neurosciences, The Feinstein Institutes for Medical Research, 350 Community Drive, Manhasset, NY, 11030, USA

    Katharina A. Schindlbeck, Chris C. Tang, Yoon Young Choi, Vijay Dhawan & David Eidelberg

  2. Division of Movement Disorders, Columbia University Medical Center, New York, NY, USA

    Deepak K. Gupta, Sarah A. O’Shea & Stanley Fahn

  3. Larner College of Medicine, University of Vermont Medical Center, Burlington, VT, USA

    Deepak K. Gupta

  4. Department of Neurology, Boston University School of Medicine, Boston University, Boston, MA, USA

    Sarah A. O’Shea

  5. Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, CA, USA

    Kathleen L. Poston

  6. Division of Neuropathology, Columbia University Medical Center, New York, NY, USA

    Jean-Paul Vonsattel

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  1. Katharina A. Schindlbeck
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Contributions

K.A.S., D.K.G., J.-P.V., S.F., and D.E. contributed to the conception and design of the study; K.A.S., D.K.G., C.C.T., S.A.O., K.L.P., Y.Y.C., V.D., J.-P.V., S.F., and D.E. contributed to the acquisition and analysis of the data; and K.A.S. and D.E. drafted the manuscript and prepared the figures; D.K.G., C.C.T., S.A.O., K.L.P., Y.Y.C., V.D., J.-P.V., and S.F. reviewed the manuscript for intellectual content.

Corresponding author

Correspondence to David Eidelberg.

Ethics declarations

Ethics approval

Ethical permission for this study was obtained from the Institutional Review Board of the participating institutions (Columbia University and Northwell Health).

Consent to participate

A waiver of consent was granted for this study.

Conflict of interest

K.L.P. has received clinical trial grants from Sanofi and AstraZeneca (unrelated to manuscript) and consulting fees from Allergan and CuraSen (unrelated to manuscript). D.E. serves on the scientific advisory boards of and has received fees from The Michael J. Fox Foundation for Parkinson’s Research and Ovid Therapeutics (unrelated to manuscript); receives consulting fees from MeiraGTx (unrelated to manuscript); has received grants from NIH (NINDS, NIAID) (unrelated to manuscript); and is the coinventor of patents re: Markers for use in screening patients for nervous system dysfunction and a method and apparatus for using same, without financial gain. All other authors disclose no relevant conflict of interest.

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This article is part of the Topical Collection on Neurology – Movement disorders.

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Schindlbeck, K.A., Gupta, D.K., Tang, C.C. et al. Neuropathological correlation supports automated image-based differential diagnosis in parkinsonism. Eur J Nucl Med Mol Imaging 48, 3522–3529 (2021). https://doi.org/10.1007/s00259-021-05302-6

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  • DOI: https://doi.org/10.1007/s00259-021-05302-6

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