4.7 Article

Differential Diagnosis of Parkinsonism Based on Deep Metabolic Imaging Indices

期刊

JOURNAL OF NUCLEAR MEDICINE
卷 63, 期 11, 页码 1741-1747

出版社

SOC NUCLEAR MEDICINE INC
DOI: 10.2967/jnumed.121.263029

关键词

Parkinson disease; atypical parkinsonian syndrome; differential diagnosis; deep learning; deep metabolic imaging indices

资金

  1. National Natural Science Foundation of China [81771483, 81671239, 81361120393, 81401135, 81971641, 81902282, 91949118, 81771372]
  2. Ministry of Science and Technology of China [2016YFC1306504]
  3. Shanghai Municipal Science and Technology Major Project [2017SHZDZX01, 2018SHZDZX03]
  4. ZJ Lab
  5. Youth Medical Talents-Medical Imaging Practitioner Program by Shanghai Municipal Health Commission
  6. Shanghai Medical and Health Development Foundation [SHWRS(2020)_087]
  7. Shanghai Sailing Program by Shanghai Science and Technology Committee [18YF1403100]
  8. Swiss National Science Foundation [188350]
  9. Jacques & Gloria Gossweiler Foundation
  10. Siemens Healthineers

向作者/读者索取更多资源

This study developed metabolic imaging indices based on deep learning to support the differential diagnosis of early idiopathic Parkinson disease (IPD) and atypical parkinsonian syndromes. The proposed indices showed high sensitivity and specificity in the blind-test cohort and were robust when dealing with discrepancies between populations and imaging acquisitions.
The clinical presentations of early idiopathic Parkinson disease (IPD) substantially overlap with those of atypical parkinsonian syndromes such as multiple system atrophy (MSA) and progressive supranuclear palsy (PSP). This study aimed to develop metabolic imaging indices based on deep learning to support the differential diagnosis of these conditions. Methods: A benchmark Huashan parkinsonian PET imaging (HPPI, China) database including 1,275 parkinsonian patients and 863 nonparkinsonian subjects with 18F-FDG PET images was established to support artificial intelligence development. A 3-dimensional deep convolutional neural network was developed to extract deep metabolic imaging (DMI) indices and blindly evaluated in an independent cohort with longitudinal follow-up from the HPPI and an external German cohort of 90 parkinsonian patients with different imaging acquisition protocols. Results: The proposed DMI indices had less ambiguity space in the differential diagnosis. They achieved sensitivities of 98.1%, 88.5%, and 84.5%, and specificities of 90.0%, 99.2%, and 97.8%, respectively, for the diagnosis of IPD, MSA, and PSP in the blind-test cohort. In the German cohort, they resulted in sensitivities of 94.1%, 82.4%, and 82.1%, and specificities of 84.0%, 99.9%, and 94.1%, respectively. Using the PET scans independently achieved a performance comparable to the integration of demographic and clinical information into the DMI indices. Conclusion: The DMI indices developed on the HPPI database show the potential to provide an early and accurate differential diagnosis for parkinsonism and are robust when dealing with discrepancies between populations and imaging acquisitions.

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