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Identification of Parkinson’s disease and multiple system atrophy using multimodal PET/MRI radiomics

  • Nuclear Medicine
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To construct a machine learning model for differentiating Parkinson’s disease (PD) and multiple system atrophy (MSA) by using multimodal PET/MRI radiomics and clinical characteristics.

Methods

One hundred and nineteen patients (81 with PD and 38 with MSA) underwent brain PET/CT and MRI to obtain metabolic images ([18F]FDG, [11C]CFT PET) and structural MRI (T1WI, T2WI, and T2-FLAIR). Image analysis included automatic segmentation on MRI, co-registration of PET images onto the corresponding MRI. Radiomics features were then extracted from the putamina and caudate nuclei and selected to construct predictive models. Moreover, based on PET/MRI radiomics and clinical characteristics, we developed a nomogram. Receiver operating characteristic (ROC) curves were performed to evaluate the performance of the models. Decision curve analysis (DCA) was employed to access the clinical usefulness of the models.

Results

The combined PET/MRI radiomics model of five sequences outperformed monomodal radiomics models alone. Further, PET/MRI radiomics-clinical combined model could perfectly distinguish PD from MSA (AUC = 0.993), which outperformed the clinical model (AUC = 0.923, p = 0.028) in training set, with no significant difference in test set (AUC = 0.860 vs 0.917, p = 0.390). However, no significant difference was found between PET/MRI radiomics-clinical model and PET/MRI radiomics model in training (AUC = 0.988, p = 0.276) and test sets (AUC = 0.860 vs 0.845, p = 0.632). DCA demonstrated the highest clinical benefit of PET/MRI radiomics-clinical model.

Conclusions

Our study indicates that multimodal PET/MRI radiomics could achieve promising performance to differentiate between PD and MSA in clinics.

Clinical relevance statement

This study developed an optimal radiomics signature and construct model to distinguish PD from MSA by multimodal PET/MRI imaging methods in clinics for parkinsonian syndromes, which achieved an excellent performance.

Key Points

•Multimodal PET/MRI radiomics from putamina and caudate nuclei increase the diagnostic efficiency for distinguishing PD from MSA.

•The radiomics-based nomogram was developed to differentiate between PD and MSA.

•Combining PET/MRI radiomics-clinical model achieved promising performance to identify PD and MSA.

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Abbreviations

[18F]FDG:

18F-Fluorodeoxyglucose

AUC:

Area under the curve

DAT:

Dopamine transporter

DCA:

Decision curve analysis

MRI:

Magnetic resonance imaging

MSA:

Multiple system atrophy

PD:

Parkinson’s disease

PET:

Positron emission tomography

ROC:

Receiver operating characteristic

References

  1. Hughes AJ, Daniel SE, Ben-Shlomo Y, Lees AJ (2002) The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 125:861–870

    Article  PubMed  Google Scholar 

  2. Peralta C, Biafore F, Depetris TS, Bastianello M (2019) Recent advancement and clinical implications of 18FDG-PET in Parkinson’s disease, atypical parkinsonisms, and other movement disorders. Curr Neurol Neurosci Rep 19:56

    Article  PubMed  Google Scholar 

  3. McKay JH, Cheshire WP (2018) First symptoms in multiple system atrophy. Clin Auton Res 28:215–221

    Article  PubMed  PubMed Central  Google Scholar 

  4. Adler CH, Beach TG, Hentz JG et al (2014) Low clinical diagnostic accuracy of early vs advanced Parkinson disease: clinicopathologic study. Neurology 83:406–412

    Article  PubMed  PubMed Central  Google Scholar 

  5. Fanciulli A, Wenning GK (2015) Multiple-system atrophy. N Engl J Med 372:249–263

    Article  PubMed  Google Scholar 

  6. Peeraully T (2014) Multiple system atrophy. Semin Neurol 34:174–181

    Article  PubMed  Google Scholar 

  7. Heiss WD (2016) Hybrid PET/MR imaging in neurology: present applications and prospects for the future. J Nucl Med 57:993–995

    Article  CAS  PubMed  Google Scholar 

  8. Saeed U, Compagnone J, Aviv RI et al (2017) Imaging biomarkers in Parkinson’s disease and Parkinsonian syndromes: current and emerging concepts. Transl Neurodegener 6:8

    Article  PubMed  PubMed Central  Google Scholar 

  9. Poewe W, Wenning G (2002) The differential diagnosis of Parkinson’s disease. Eur J Neurol 9(Suppl 3):23–30

    Article  PubMed  Google Scholar 

  10. Zhao P, Zhang B, Gao S, Li X (2020) Clinical features, MRI, and 18F-FDG-PET in differential diagnosis of Parkinson disease from multiple system atrophy. Brain Behav 10:e01827

    Article  PubMed  PubMed Central  Google Scholar 

  11. Zhu S, Deng B, Huang Z et al (2021) “Hot cross bun” is a potential imaging marker for the severity of cerebellar ataxia in MSA-C. NPJ Parkinsons Dis 7:15

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  Google Scholar 

  13. Matthews DC, Lerman H, Lukic A et al (2018) FDG PET Parkinson’s disease-related pattern as a biomarker for clinical trials in early stage disease. Neuroimage Clin 20:572–579

    Article  PubMed  PubMed Central  Google Scholar 

  14. Akdemir UO, Tokcaer AB, Karakus A, Kapucu LO (2014) Brain 18F-FDG PET imaging in the differential diagnosis of parkinsonism. Clin Nucl Med 39:e220-226

    Article  PubMed  Google Scholar 

  15. Lambin P, Leijenaar RTH, Deist TM et al (2017) Radiomics: the bridge between medical imaging and personalized medicine. Nat Rev Clin Oncol 14:749–762

    Article  PubMed  Google Scholar 

  16. van Timmeren JE, Cester D, Tanadini-Lang S, Alkadhi H, Baessler B (2020) Radiomics in medical imaging-“how-to” guide and critical reflection. Insights Imaging 11:91

    Article  PubMed  PubMed Central  Google Scholar 

  17. Aerts HJ, Velazquez ER, Leijenaar RT et al (2014) Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 5:4006

    Article  CAS  PubMed  Google Scholar 

  18. Sun R, Limkin EJ, Vakalopoulou M et al (2018) A radiomics approach to assess tumour-infiltrating CD8 cells and response to anti-PD-1 or anti-PD-L1 immunotherapy: an imaging biomarker, retrospective multicohort study. Lancet Oncol 19:1180–1191

    Article  CAS  PubMed  Google Scholar 

  19. Han Y, Ma Y, Wu Z et al (2021) Histologic subtype classification of non-small cell lung cancer using PET/CT images. Eur J Nucl Med Mol Imaging 48:350–360

    Article  PubMed  Google Scholar 

  20. Kirienko M, Sollini M, Corbetta M et al (2021) Radiomics and gene expression profile to characterise the disease and predict outcome in patients with lung cancer. Eur J Nucl Med Mol Imaging 48:3643–3655

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Hu X, Sun X, Hu F et al (2021) Multivariate radiomics models based on (18)F-FDG hybrid PET/MRI for distinguishing between Parkinson’s disease and multiple system atrophy. Eur J Nucl Med Mol Imaging 48:3469–3481

    Article  CAS  PubMed  Google Scholar 

  22. Tupe-Waghmare P, Rajan A, Prasad S, Saini J, Pal PK, Ingalhalikar M (2021) Radiomics on routine T1-weighted MRI can delineate Parkinson’s disease from multiple system atrophy and progressive supranuclear palsy. Eur Radiol 31:8218–8227

    Article  PubMed  Google Scholar 

  23. Xu J, Xu Q, Liu S et al (2021) Computer-aided classification framework of Parkinsonian disorders using (11)C-CFT PET imaging. Front Aging Neurosci 13:792951

    Article  CAS  PubMed  Google Scholar 

  24. Sun D, Wu X, Xia Y et al (2021) Differentiating Parkinson’s disease motor subtypes: a radiomics analysis based on deep gray nuclear lesion and white matter. Neurosci Lett 760:136083

    Article  CAS  PubMed  Google Scholar 

  25. Wu Y, Jiang JH, Chen L et al (2019) Use of radiomic features and support vector machine to distinguish Parkinson’s disease cases from normal controls. Ann Transl Med 7:773

    Article  PubMed  PubMed Central  Google Scholar 

  26. Salmanpour MR, Shamsaei M, Saberi A, Hajianfar G, Soltanian-Zadeh H, Rahmim A (2021) Robust identification of Parkinson’s disease subtypes using radiomics and hybrid machine learning. Comput Biol Med 129:104142

    Article  PubMed  Google Scholar 

  27. 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  PubMed  PubMed Central  Google Scholar 

  28. Buchert R, Lange C, Suppa P et al (2018) Magnetic resonance imaging-based hippocampus volume for prediction of dementia in mild cognitive impairment: why does the measurement method matter so little? Alzheimers Dement 14:976–978

    Article  PubMed  Google Scholar 

  29. Johnson H, Harris G, Williams K (2007) BRAINSFit: mutual information rigid registrations of whole-brain 3D images, using the Insight Toolkit. Insight J. http://hdl.handle.net/1926/1291

  30. Ashburner J, Friston KJ (2005) Unified segmentation. Neuroimage 26:839–851

    Article  PubMed  Google Scholar 

  31. Mandal PK, Mahajan R, Dinov ID (2012) Structural brain atlases: design, rationale, and applications in normal and pathological cohorts. J Alzheimers Dis 31(Suppl 3):S169-188

    Article  PubMed  PubMed Central  Google Scholar 

  32. Tong H, Sun J, Fang J et al (2022) A machine learning model based on PET/CT radiomics and clinical characteristics predicts tumor immune profiles in non-small cell lung cancer: a retrospective multicohort study. Front Immunol 13:859323

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ewert S, Horn A, Finkel F, Li N, Kuhn AA, Herrington TM (2019) Optimization and comparative evaluation of nonlinear deformation algorithms for atlas-based segmentation of DBS target nuclei. Neuroimage 184:586–598

    Article  PubMed  Google Scholar 

  34. Zwirner J, Mobius D, Bechmann I et al (2017) Subthalamic nucleus volumes are highly consistent but decrease age-dependently-a combined magnetic resonance imaging and stereology approach in humans. Hum Brain Mapp 38:909–922

    Article  PubMed  Google Scholar 

  35. Chakravarty MM, Steadman P, van Eede MC et al (2013) Performing label-fusion-based segmentation using multiple automatically generated templates. Hum Brain Mapp 34:2635–2654

    Article  PubMed  Google Scholar 

  36. Ruan W, Sun X, Hu X et al (2020) Regional SUV quantification in hybrid PET/MR, a comparison of two atlas-based automatic brain segmentation methods. EJNMMI Res 10:60

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Brajkovic L, Kostic V, Sobic-Saranovic D et al (2017) The utility of FDG-PET in the differential diagnosis of Parkinsonism. Neurol Res 39:675–684

    Article  CAS  PubMed  Google Scholar 

  38. Meyer PT, Frings L, Rucker G, Hellwig S (2017) (18)F-FDG PET in parkinsonism: differential diagnosis and evaluation of cognitive impairment. J Nucl Med 58:1888–1898

    Article  CAS  PubMed  Google Scholar 

  39. Xian WB, Shi XC, Luo GH, Yi C, Zhang XS, Pei Z (2021) Co-registration analysis of fluorodopa and fluorodeoxyglucose positron emission tomography for differentiating multiple system atrophy parkinsonism type from Parkinson’s disease. Front Aging Neurosci 13:648531

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Berardelli A, Wenning GK, Antonini A et al (2013) EFNS/MDS-ES/ENS [corrected] recommendations for the diagnosis of Parkinson’s disease. Eur J Neurol 20:16–34

    Article  CAS  PubMed  Google Scholar 

  41. Xu J, Zhang M (2019) Use of magnetic resonance imaging and artificial intelligence in studies of diagnosis of Parkinson’s disease. ACS Chem Neurosci 10:2658–2667

    Article  CAS  PubMed  Google Scholar 

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Funding

This work was supported by grants from the Talent Innovation Ability Training Program of Daping Hospital (2019CXLCC010, 2019CXLCB014), Science and Technology Innovation Ability Enhancement Project of Army Medical University (2022XJS29), Chongqing Science and Health Joint Medical Research Project-Young and Middle-aged High-level Talent Project (2023GDRC002), National Natural Science Foundation of China (81801672), and Chongqing Clinical Research Centre of Imaging and Nuclear Medicine (CSTC2015YFPT-gcjsyjzx0175).

Author information

Author notes

  1. Jinju Sun, Chao Cong, and Xinpeng Li contributed equally to this work.

Authors and Affiliations

  1. Department of Nuclear Medicine, Daping Hospital, Army Medical University, Chongqing, China

    Jinju Sun, Chao Cong, Weicheng Zhou, Renxiang Xia, Yi Wang & Xiao Chen

  2. School of Electrical and Electronic Engineering, Chongqing University of Technology, Chongqing, China

    Chao Cong

  3. Department of Neurology, Daping Hospital, Army Medical University, Chongqing, China

    Xinpeng Li & Zhiqiang Xu

  4. GE Healthcare, Shanghai, China

    Huan Liu

  5. Chongqing Clinical Research Center for Imaging and Nuclear Medicine, Chongqing, China

    Xiao Chen

Authors
  1. Jinju Sun
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Corresponding authors

Correspondence to Zhiqiang Xu or Xiao Chen.

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Guarantor

The scientific guarantor of this publication is Xiao Chen.

Conflict of interest

Author Huan Liu was employed by GE Healthcare. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Statistics and biometry

One of the authors (Huan Liu) has significant statistical expertise.

Informed consent

The requirement of informed consent was waived by the Ethics Committee of Daping Hospital, Army Medical University, because this was a retrospective study.

Ethical approval

This retrospective study was conducted with the approval of the Ethical Committee of Daping Hospital, Army Medical University. This study’s use of human subjects complies with the Declaration of Helsinki.

Methodology

•retrospective

•case–control study

•performed at one institution

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Sun, J., Cong, C., Li, X. et al. Identification of Parkinson’s disease and multiple system atrophy using multimodal PET/MRI radiomics. Eur Radiol 34, 662–672 (2024). https://doi.org/10.1007/s00330-023-10003-9

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  • DOI: https://doi.org/10.1007/s00330-023-10003-9

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