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Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns’ based approach

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

Abstract

Purpose

Differentiation among parkinsonian syndromes may be clinically challenging, especially at early disease stages. In this study, we used 18F-FDG-PET brain imaging combined with an automated image classification algorithm to classify parkinsonian patients as Parkinson’s disease (PD) or as an atypical parkinsonian syndrome (APS) at the time when the clinical diagnosis was still uncertain. In addition to validating the algorithm, we assessed its utility in a “real-life” clinical setting.

Methods

One hundred thirty-seven parkinsonian patients with uncertain clinical diagnosis underwent 18F-FDG-PET and were classified using an automated image-based algorithm. For 66 patients in cohort A, the algorithm-based diagnoses were compared with their final clinical diagnoses, which were the gold standard for cohort A and were made 2.2 ± 1.1 years (mean ± SD) later by a movement disorder specialist. Seventy-one patients in cohort B were diagnosed by general neurologists, not strictly following diagnostic criteria, 2.5 ± 1.6 years after imaging. The clinical diagnoses were compared with the algorithm-based ones, which were considered the gold standard for cohort B.

Results

Image-based automated classification of cohort A resulted in 86.0% sensitivity, 92.3% specificity, 97.4% positive predictive value (PPV), and 66.7% negative predictive value (NPV) for PD, and 84.6% sensitivity, 97.7% specificity, 91.7% PPV, and 95.5% NPV for APS. In cohort B, general neurologists achieved 94.7% sensitivity, 83.3% specificity, 81.8% PPV, and 95.2% NPV for PD, while 88.2%, 76.9%, 71.4%, and 90.9% for APS.

Conclusion

The image-based algorithm had a high specificity and the predictive values in classifying patients before a final clinical diagnosis was reached by a specialist. Our data suggest that it may improve the diagnostic accuracy by 10–15% in PD and 20% in APS when a movement disorder specialist is not easily available.

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Authors and Affiliations

  1. Department of Neurology, UMC Ljubljana, Zaloška cesta 2, 1000, Ljubljana, Slovenia

    Tomaž Rus, Zvezdan Pirtošek & Maja Trošt

  2. Medical Faculty, University of Ljubljana, Vrazov trg 2, 1000, Ljubljana, Slovenia

    Tomaž Rus, Zvezdan Pirtošek & Maja Trošt

  3. Department of Nuclear Medicine, UMC Ljubljana, Zaloška cesta 7, 1000, Ljubljana, Slovenia

    Petra Tomše, Luka Jensterle, Marko Grmek & Maja Trošt

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

    David Eidelberg & Chris Tang

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  1. Tomaž Rus
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  2. Petra Tomše
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  7. Chris Tang
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Correspondence to Tomaž Rus.

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Conflict of interest

DE serves as a member of scientific advisory board of Ovid Therapeutics (receives personal fees) and has served on the scientific advisory board of The Michael J. Fox Foundation (has received honoraria); is a consultant to MeiraGTx (has received fees); is listed as coinventor of patents for markers for use in screening patients for nervous system dysfunction as well as a method and apparatus for using same (without financial gain); has received research support from the National Institutes of Health (NINDS, NIAID) and The Michael J. Fox Foundation for Parkinson’s Research. The other authors have no relevant conflicts of interest to declare.

Informed consent

Written informed consent was obtained from the participating subjects.

Ethical approval

All procedures performed in the study involving human participants were in accordance with the ethical standards of the national research committee (The National Medical Ethics Committee of the Republic of Slovenia, 0120-351/2015-2, KME 70/07/15) and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

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Rus, T., Tomše, P., Jensterle, L. et al. Differential diagnosis of parkinsonian syndromes: a comparison of clinical and automated - metabolic brain patterns’ based approach. Eur J Nucl Med Mol Imaging 47, 2901–2910 (2020). https://doi.org/10.1007/s00259-020-04785-z

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  • DOI: https://doi.org/10.1007/s00259-020-04785-z

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