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A prospective multi-centre study of susceptibility map-weighted MRI for the diagnosis of neurodegenerative parkinsonism

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Abstract

Objectives

This study aimed to compare susceptibility map-weighted imaging (SMwI) using various MRI machines (three vendors) with N-3-fluoropropyl-2-β-carbomethoxy-3-β-(4-iodophe nyl)nortropane (18F-FP-CIT) PET in the diagnosis of neurodegenerative parkinsonism in a multi-centre setting.

Methods

We prospectively recruited 257 subjects, including 157 patients with neurodegenerative parkinsonism, 54 patients with non-neurodegenerative parkinsonism, and 46 healthy subjects from 10 hospitals between November 2019 and October 2020. All participants underwent both SMwI and 18F-FP-CIT PET. SMwI was interpreted by two independent reviewers for the presence or absence of abnormalities in nigrosome 1, and discrepancies were resolved by consensus. 18F-FP-CIT PET was used as the reference standard. Inter-observer agreement was tested using Cohen’s kappa coefficient. McNemar’s test was used to test the agreement between the interpretations of SMwI and 18F-FP-CIT PET per participant and substantia nigra (SN).

Results

The inter-observer agreement was 0.924 and 0.942 per SN and participant, respectively. The diagnostic sensitivity of SMwI was 97.9% and 99.4% per SN and participant, respectively; its specificity was 95.9% and 95.2%, respectively, and its accuracy was 97.1% and 97.7%, respectively. There was no significant difference between the results of SMwI and 18F-FP-CIT PET (p > 0.05, for both SN and participant).

Conclusions

This study demonstrated that the high diagnostic performance of SMwI was maintained in a multi-centre setting with various MRI scanners, suggesting the generalisability of SMwI for determining nigrostriatal degeneration in patients with parkinsonism.

Key Points

• Susceptibility map-weighted imaging helps clinicians to predict nigrostriatal degeneration.

• The protocol for susceptibility map-weighted imaging can be standardised across MRI vendors.

• Susceptibility map-weighted imaging showed diagnostic performance comparable to that of dopamine transporter PET in a multi-centre setting with various MRI scanners.

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Abbreviations

18F-FP-CIT:

N-3-fluoropropyl-2-β-carbomethoxy-3-β-(4-iodophenyl)nortropane

DAT:

Dopamine transporter

H&Y:

Hoehn and Yahr

IPD:

Idiopathic Parkinson’s disease

MSA:

Multiple system atrophy

PSP:

Progressive supranuclear palsy

QSM:

Quantitative susceptibility map**

SMwI:

Susceptibility map-weighted imaging

SN:

Substantia nigra

References

  1. Kwon DH, Kim JM, Oh SH et al (2012) Seven-Tesla magnetic resonance images of the substantia nigra in Parkinson disease. Ann Neurol 71:267–277

    Article  Google Scholar 

  2. Schwarz ST, Afzal M, Morgan PS, Bajaj N, Gowland PA, Auer DP (2014) The ‘swallow tail’ appearance of the healthy nigrosome - a new accurate test of Parkinson’s disease: a case-control and retrospective cross-sectional MRI study at 3T. PLoS One 9:e93814

  3. Cosottini M, Frosini D, Pesaresi I et al (2014) MR imaging of the substantia nigra at 7 T enables diagnosis of Parkinson disease. Radiology 271:831–838

    Article  Google Scholar 

  4. Cosottini M, Frosini D, Pesaresi I et al (2015) Comparison of 3T and 7T susceptibility-weighted angiography of the substantia nigra in diagnosing Parkinson disease. AJNR Am J Neuroradiol 36:461–466

    Article  CAS  Google Scholar 

  5. Reiter E, Mueller C, Pinter B et al (2015) Dorsolateral nigral hyperintensity on 3.0T susceptibility-weighted imaging in neurodegenerative Parkinsonism. Mov Disord 30:1068–1076

    Article  CAS  Google Scholar 

  6. Noh Y, Sung YH, Lee J, Kim EY (2015) Nigrosome 1 detection at 3T MRI for the diagnosis of early-stage idiopathic Parkinson disease: assessment of diagnostic accuracy and agreement on imaging asymmetry and clinical laterality. AJNR Am J Neuroradiol 36:2010–2016

    Article  CAS  Google Scholar 

  7. Bae YJ, Kim JM, Kim E et al (2016) Loss of nigral hyperintensity on 3 Tesla MRI of parkinsonism: comparison with (123) I-FP-CIT SPECT. Mov Disord 31:684–692

    Article  CAS  Google Scholar 

  8. Mahlknecht P, Krismer F, Poewe W, Seppi K (2017) Meta-analysis of dorsolateral nigral hyperintensity on magnetic resonance imaging as a marker for Parkinson’s disease. Mov Disord 32:619–623

    Article  CAS  Google Scholar 

  9. Cho SJ, Bae YJ, Kim JM et al (2021) Iron-sensitive magnetic resonance imaging in Parkinson’s disease: a systematic review and meta-analysis. J Neurol. https://doi.org/10.1007/s00415-021-10582-x

    Article  PubMed  Google Scholar 

  10. Rajput AH, Rozdilsky B, Rajput A (1991) Accuracy of clinical diagnosis in parkinsonism–a prospective study. Can J Neurol Sci 18:275–278

    Article  CAS  Google Scholar 

  11. Hughes AJ, Daniel SE, Kilford L, Lees AJ (1992) Accuracy of clinical diagnosis of idiopathic Parkinson’s disease: a clinico-pathological study of 100 cases. J Neurol Neurosurg Psychiatry 55:181–184

    Article  CAS  Google Scholar 

  12. Litvan I, MacIntyre A, Goetz CG et al (1998) Accuracy of the clinical diagnoses of Lewy body disease, Parkinson disease, and dementia with Lewy bodies: a clinicopathologic study. Arch Neurol 55:969–978

    Article  CAS  Google Scholar 

  13. Tolosa E, Wenning G, Poewe W (2006) The diagnosis of Parkinson’s disease. Lancet Neurol 5:75–86

    Article  Google Scholar 

  14. Kim PH, Lee DH, Suh CH, Kim M, Shim WH, Kim SJ (2021) Diagnostic performance of loss of nigral hyperintensity on susceptibility-weighted imaging in parkinsonism: an updated meta-analysis. Eur Radiol 31:6342–6352

    Article  Google Scholar 

  15. Gho SM, Liu C, Li W et al (2014) Susceptibility map-weighted imaging (SMWI) for neuroimaging. Magn Reson Med 72:337–346

    Article  Google Scholar 

  16. Nam Y, Gho SM, Kim DH, Kim EY, Lee J (2017) Imaging of nigrosome 1 in substantia nigra at 3T using multiecho susceptibility map-weighted imaging (SMWI). J Magn Reson Imaging 46:528–536

    Article  Google Scholar 

  17. Bae YJ, Song YS, Choi BS, Kim JM, Nam Y, Kim JH (2021) Comparison of susceptibility-weighted imaging and susceptibility map-weighted imaging for the diagnosis of Parkinsonism with nigral hyperintensity. Eur J Radiol 134:109398

    Article  Google Scholar 

  18. Sung YH, Lee J, Nam Y et al (2019) Initial diagnostic workup of parkinsonism: dopamine transporter positron emission tomography versus susceptibility map-weighted imaging at 3T. Parkinsonism Relat Disord 62:171–178

    Article  Google Scholar 

  19. Postuma RB, Berg D, Stern M et al (2015) MDS clinical diagnostic criteria for Parkinson’s disease. Mov Disord 30:1591–1601

    Article  Google Scholar 

  20. Litvan I, Agid Y, Calne D et al (1996) Clinical research criteria for the diagnosis of progressive supranuclear palsy (Steele-Richardson-Olszewski syndrome): report of the NINDS-SPSP international workshop. Neurology 47:1–9

    Article  CAS  Google Scholar 

  21. Gilman S, Wenning GK, Low PA et al (2008) Second consensus statement on the diagnosis of multiple system atrophy. Neurology 71:670–676

    Article  CAS  Google Scholar 

  22. Elble RJ (2000) Diagnostic criteria for essential tremor and differential diagnosis. Neurology 54:S2-6

    Article  CAS  Google Scholar 

  23. Sung YH, Noh Y, Lee J, Kim EY (2016) Drug-induced parkinsonism versus idiopathic Parkinson disease: utility of nigrosome 1 with 3-T imaging. Radiology 279:849–858

    Article  Google Scholar 

  24. Korczyn AD (2015) Vascular parkinsonism–characteristics, pathogenesis and treatment. Nat Rev Neurol 11:319–326

    Article  Google Scholar 

  25. Erro R, Schneider SA, Stamelou M, Quinn NP, Bhatia KP (2016) What do patients with scans without evidence of dopaminergic deficit (SWEDD) have? New evidence and continuing controversies. J Neurol Neurosurg Psychiatry 87:319–323

    Article  Google Scholar 

  26. Hogl B, Stefani A (2017) REM sleep behavior disorder (RBD): update on diagnosis and treatment. Somnologie (Berl) 21:1–8

  27. Shin DH, Heo H, Song S et al (2021) Automated assessment of the substantia nigra on susceptibility map-weighted imaging using deep convolutional neural networks for diagnosis of Idiopathic Parkinson’s disease. Parkinsonism Relat Disord 85:84–90

    Article  Google Scholar 

  28. Li W, Wang N, Yu F et al (2015) A method for estimating and removing streaking artifacts in quantitative susceptibility map**. Neuroimage 108:111–122

    Article  Google Scholar 

  29. Sung YH, Lee J, Nam Y et al (2018) Differential involvement of nigral subregions in idiopathic Parkinson’s disease. Hum Brain Mapp 39:542–553

    Article  Google Scholar 

  30. Benamer TS, Patterson J, Grosset DG et al (2000) Accurate differentiation of parkinsonism and essential tremor using visual assessment of [123I]-FP-CIT SPECT imaging: the [123I]-FP-CIT study group. Mov Disord 15:503–510

    Article  CAS  Google Scholar 

  31. Bae YJ, Song YS, Kim JM et al (2021) Determining the degree of dopaminergic denervation based on the loss of nigral hyperintensity on SMWI in Parkinsonism. AJNR Am J Neuroradiol 42:681–687

    Article  CAS  Google Scholar 

  32. Wang N, Liu XL, Li L et al (2021) Screening for early-stage Parkinson’s disease: swallow tail sign on MRI susceptibility map-weighted images compared with PET. J Magn Reson Imaging 53:722–730

    Article  Google Scholar 

  33. De Pietro Franco Zorzenon C, Almeida Antonio Bienes GH, Duarte Alves E, TobaruTibana LA, Carrete Junior H, BallalaiFerraz H (2020) Magnetic resonance imaging evaluation of nigrosome 1 and neuromelanin can assist Parkinson’s disease diagnosis, but requires an expert neuroradiologist. Parkinsonism Relat Disord 83:8–12

    Article  Google Scholar 

Download references

Funding

This work was supported by a grant from the Heuron Co., Ltd; and a grant from the Basic Research in Science and Engineering Program through the National Research Foundation (NRF) funded by the Korean government (MSIT) (No. 2021R1C1C1003676).

Author information

Authors and Affiliations

  1. Department of Neurology, Gil Medical Center, Gachon University College of Medicine, Incheon, Republic of Korea

    Young Hee Sung

  2. Department of Neurology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

    Joong-Seok Kim & Sang-Won Yoo

  3. Department of Radiology, College of Medicine, The Catholic University of Korea, Seoul, Republic of Korea

    Na-Young Shin

  4. Division of Biomedical Engineering, Hankuk University of Foreign Studies, Yongin-si, Gyeonggi-do, Republic of Korea

    Yoonho Nam

  5. Department of Neurology, College of Medicine, Kyung Hee University, Seoul, Republic of Korea

    Tae-Beom Ahn & Dallah Yoo

  6. Department of Radiology, College of Medicine, Kyung Hee University, Seoul, Republic of Korea

    Kyung Mi Lee & Hyug-Gi Kim

  7. Department of Neurology and Parkinson’s Disease Centre, Korea University Guro Hospital, Korea University College of Medicine, Seoul, Republic of Korea

    Seong-Beom Koh, **hee Kim & Ilsoo Kim

  8. Department of Neurology, Center for Movement Disorders and Neurodegenerative Diseases, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, Republic of Korea

    Do-Young Kwon

  9. Department of Radiology, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, Republic of Korea

    Younghen Lee

  10. Department of Nuclear Medicine, Korea University Ansan Hospital, Ansan-si, Gyeonggi-do, Republic of Korea

    Chulhan Kim

  11. Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Sun Ju Chung, Sungyang Jo & Seung Hyun Lee

  12. Department of Radiology and Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea

    Sang Joon Kim & Minjae Kim

  13. Department of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

    Chul Hyoung Lyoo

  14. Department of Neurology, Wonju Severance Christian Hospital, Yonsei University Wonju College of Medicine, Gangwon do, Wonju, Republic of Korea

    Min Seok Baek

  15. Department of Neurology, Dongtan Sacred Heart Hospital, Hallym University College of Medicine, Hwaseong-si, Gyeonggi-do, Republic of Korea

    Suk Yun Kang

  16. Department of Radiology, Hallym University Dongtan Sacred Heart Hospital, Hwaseong-si, Gyeonggi-do, Republic of Korea

    Suk Ki Chang, Sang-Won Jo & Seun Ah Lee

  17. Department of Neurology, Hallym University Sacred Heart Hospital, Hallym University College of Medicine, Anyang-si, Gyeonggi-do, Republic of Korea

    Hyeo-il Ma & Young Eun Kim

  18. Department of Radiology, Hallym University Sacred Heart Hospital, Anyang-si, Gyeonggi-do, Republic of Korea

    Eun Soo Kim

  19. Department of Neurology, Center for Neurodegenerative Disorders, Yongin Severance Hospital, Yonsei University Health System, Yonsei University College of Medicine, Yongin-si, Gyeonggi-do, Republic of Korea

    Yun Joong Kim

  20. Department of Neurology, CHA Bundang Medical Center, CHA University, Seongnam-si, Gyeonggi-do, Republic of Korea

    Hyun Sook Kim & Min-Hee Woo

  21. Radiology Department, CHA Bundang Medical Center, CHA University, Seongnam-si, Gyeonggi-do, Republic of Korea

    Hye Jeong Choi

  22. Department of Radiology, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81, Irwon-ro, Gangnam-gu, Seoul, 06351, Republic of Korea

    Eung Yeop Kim

Authors
  1. Young Hee Sung
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  2. Joong-Seok Kim
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  4. Na-Young Shin
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  7. Dallah Yoo
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  8. Kyung Mi Lee
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  11. **hee Kim
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  14. Younghen Lee
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  15. Chulhan Kim
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  16. Sun Ju Chung
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  17. Sungyang Jo
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  18. Seung Hyun Lee
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  19. Sang Joon Kim
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  20. Minjae Kim
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  22. Min Seok Baek
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  23. Suk Yun Kang
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  24. Suk Ki Chang
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  26. Seun Ah Lee
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  27. Hyeo-il Ma
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  28. Young Eun Kim
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  29. Eun Soo Kim
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  30. Yun Joong Kim
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  31. Hyun Sook Kim
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  32. Min-Hee Woo
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  34. Eung Yeop Kim
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Corresponding author

Correspondence to Eung Yeop Kim.

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Guarantor

The scientific guarantor of this publication is Eung Yeop Kim.

Conflict of interest

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

Statistics and biometry

No complex statistical methods were necessary for this paper.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

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Institutional Review Board approval was obtained.

Methodology

• prospective

• diagnostic study

• multicentre study

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Sung, Y.H., Kim, JS., Yoo, SW. et al. A prospective multi-centre study of susceptibility map-weighted MRI for the diagnosis of neurodegenerative parkinsonism. Eur Radiol 32, 3597–3608 (2022). https://doi.org/10.1007/s00330-021-08454-z

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  • DOI: https://doi.org/10.1007/s00330-021-08454-z

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