Skip to content
Licensed Unlicensed Requires Authentication Published by De Gruyter May 3, 2019

Metabolic pattern analysis of 18F-FDG PET as a marker for Parkinson’s disease: a systematic review and meta-analysis

  • Si-Chun Gu , Qing Ye EMAIL logo and Can-Xing Yuan EMAIL logo

Abstract

A large number of articles have assessed the diagnostic accuracy of the metabolic pattern analysis of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) in Parkinson’s disease (PD); however, different studies involved small samples with various controls and methods, leading to discrepant conclusions. This study aims to consolidate the available observational studies and provide a comprehensive evaluation of the clinical utility of 18F-FDG PET for PD. The methods included a systematic literature search and a hierarchical summary receiver operating characteristic approach. Sensitivity analyses according to different pattern analysis methods (statistical parametric mapping versus scaled subprofile modeling/principal component analysis) and control population [healthy controls (HCs) versus atypical parkinsonian disorder (APD) patients] were performed to verify the consistency of the main results. Additional analyses for multiple system atrophy (MSA) and progressive supranuclear palsy (PSP) were conducted. Fifteen studies comprising 1446 subjects (660 PD patients, 499 APD patients, and 287 HCs) were included. The overall diagnostic accuracy of 18F-FDG in differentiating PD from APDs and HCs was quite high, with a pooled sensitivity of 0.88 [95% confidence interval (95% CI), 0.85–0.91] and a pooled specificity of 0.92 (95% CI, 0.89–0.94), with sensitivity analyses indicating statistically consistent results. Additional analyses showed an overall sensitivity and specificity of 0.87 (95% CI, 0.76–0.94) and 0.93 (95% CI, 0.89–0.96) for MSA and 0.91 (95% CI, 0.78–0.95) and 0.96 (95% CI, 0.92–0.98) for PSP. Our study suggests that the metabolic pattern analysis of 18F-FDG PET has high diagnostic accuracy in the differential diagnosis of parkinsonian disorders.

Award Identifier / Grant number: 2017YFC1310300

Award Identifier / Grant number: 81673726

Funding statement: We acknowledge all the authors for their helpful comments on this article. This work was supported by the National Key R&D Program of China (2017YFC1310300) and the National Natural Science Foundation of China (Funder Id: 10.13039/501100001809, 81673726). We declare that the funder had no role neither in study design, data collection and analysis, nor in our decision to publish and preparation of the manuscript.

  1. Conflict of interest statement: None to declare.

References

Asanuma, K., Tang, C., Ma, Y., Dhawan, V., Mattis, P., Edwards, C., Kaplitt, M.G., Feigin, A., and Eidelberg, D. (2006). Network modulation in the treatment of Parkinson’s disease. Brain 129, 2667–2678.10.1093/brain/awl162Search in Google Scholar

Attwell, D. and Iadecola, C. (2002). The neural basis of functional brain imaging signals. Trends Neurosci. 25, 621–625.10.1016/S0166-2236(02)02264-6Search in Google Scholar

Bensimon, G., Ludolph, A., Agid, Y., Vidailhet, M., Payan, C., Leigh, P.N., and Group, N.S. (2009). Riluzole treatment, survival and diagnostic criteria in Parkinson plus disorders: the NNIPPS study. Brain 132, 156–171.10.1093/brain/awn291Search in Google Scholar PubMed PubMed Central

Boeve, B.F., Lang, A.E., and Litvan, I. (2003). Corticobasal degeneration and its relationship to progressive supranuclear palsy and frontotemporal dementia. Ann. Neurol. 54, S15–S19.10.1002/ana.10570Search in Google Scholar PubMed

Brajkovic, L., Kostic, V., Sobic-Saranovic, D., Stefanova, E., Jecmenica-Lukic, M., Jesic, A., Stojiljkovic, M., Odalovic, S., Gallivanone, F., Castiglioni, I., et al. (2017). The utility of FDG-PET in the differential diagnosis of Parkinsonism. Neurol. Res. 39, 675–684.10.1080/01616412.2017.1312211Search in Google Scholar PubMed

Brooks, D.J. (2016). Molecular imaging of dopamine transporters. Ageing Res. Rev. 30, 114–121.10.1016/j.arr.2015.12.009Search in Google Scholar PubMed

de Rijk, M.C., Rocca, W.A., Anderson, D.W., Melcon, M.O., Breteler, M.M., and Maraganore, D.M. (1997). A population perspective on diagnostic criteria for Parkinson’s disease. Neurology 48, 1277–1281.10.1212/WNL.48.5.1277Search in Google Scholar PubMed

De Rosa, A., Peluso, S., De Lucia, N., Russo, P., Annarumma, I., Esposito, M., Manganelli, F., Brunetti, A., De Michele, G., and Pappata, S. (2018). Cognitive profile and 18F-fluorodeoxyglucose PET study in LRRK2-related Parkinson’s disease. Parkinsonism Relat. Disord. 47, 80–83.10.1016/j.parkreldis.2017.12.008Search in Google Scholar PubMed

Deeks, J.J., Macaskill, P., and Irwig, L. (2005). The performance of tests of publication bias and other sample size effects in systematic reviews of diagnostic test accuracy was assessed. J. Clin. Epidemiol. 58, 882–893.10.1016/j.jclinepi.2005.01.016Search in Google Scholar PubMed

Eckert, T., Barnes, A., Dhawan, V., Frucht, S., Gordon, M.F., Feigin, A.S., and Eidelberg, D. (2005). FDG PET in the differential diagnosis of parkinsonian disorders. Neuroimage 26, 912–921.10.1016/j.neuroimage.2005.03.012Search in Google Scholar PubMed

Eckert, T., Tang, C., Ma, Y., Brown, N., Lin, T., Frucht, S., Feigin, A., and Eidelberg, D. (2008). Abnormal metabolic networks in atypical parkinsonism. Mov. Disord. 23, 727–733.10.1002/mds.21933Search in Google Scholar

Eidelberg, D. (2009). Metabolic brain networks in neurodegenerative disorders: a functional imaging approach. Trends Neurosci. 32, 548–557.10.1016/j.tins.2009.06.003Search in Google Scholar

Feigin, A., Kaplitt, M.G., Tang, C., Lin, T., Mattis, P., Dhawan, V., During, M.J., and Eidelberg, D. (2007). Modulation of metabolic brain networks after subthalamic gene therapy for Parkinson’s disease. Proc. Natl. Acad. Sci. U. S. A. 104, 19559–19564.10.1073/pnas.0706006104Search in Google Scholar

Garraux, G., Phillips, C., Schrouff, J., Kreisler, A., Lemaire, C., Degueldre, C., Delcour, C., Hustinx, R., Luxen, A., Destee, A., et al. (2013). Multiclass classification of FDG PET scans for the distinction between Parkinson’s disease and atypical parkinsonian syndromes. Neuroimage Clin. 2, 883–893.10.1016/j.nicl.2013.06.004Search in Google Scholar

Granert, O., Drzezga, A.E., Boecker, H., Perneczky, R., Kurz, A., Gotz, J., van Eimeren, T., and Haussermann, P. (2015). Metabolic topology of neurodegenerative disorders: influence of cognitive and motor deficits. J. Nucl. Med. 56, 1916–1921.10.2967/jnumed.115.156067Search in Google Scholar

Grimes, D.A. and Schulz, K.F. (2005). Refining clinical diagnosis with likelihood ratios. Lancet 365, 1500–1505.10.1016/S0140-6736(05)66422-7Search in Google Scholar

Holtbernd, F., Gagnon, J.F., Postuma, R.B., Ma, Y., Tang, C.C., Feigin, A., Dhawan, V., Vendette, M., Soucy, J.P., Eidelberg, D., et al. (2014). Abnormal metabolic network activity in REM sleep behavior disorder. Neurology 82, 620–627.10.1212/WNL.0000000000000130Search in Google Scholar

Hosaka, K., Ishii, K., Sakamoto, S., Mori, T., Sasaki, M., Hirono, N., and Mori, E. (2002). Voxel-based comparison of regional cerebral glucose metabolism between PSP and corticobasal degeneration. J. Neurol. Sci. 199, 67–71.10.1016/S0022-510X(02)00102-8Search in Google Scholar

Huang, C., Mattis, P., Tang, C., Perrine, K., Carbon, M., and Eidelberg, D. (2007a). Metabolic brain networks associated with cognitive function in Parkinson’s disease. Neuroimage 34, 714–723.10.1016/j.neuroimage.2006.09.003Search in Google Scholar PubMed PubMed Central

Huang, C., Tang, C., Feigin, A., Lesser, M., Ma, Y., Pourfar, M., Dhawan, V., and Eidelberg, D. (2007b). Changes in network activity with the progression of Parkinson’s disease. Brain 130, 1834–1846.10.1093/brain/awm086Search in Google Scholar PubMed PubMed Central

Hughes, A.J., Ben-Shlomo, Y., Daniel, S.E., and Lees, A.J. (2001). What features improve the accuracy of clinical diagnosis in Parkinson’s disease: a clinicopathologic study. Neurology 57, S34–S38.10.1212/WNL.42.6.1142Search in Google Scholar

Hughes, A.J., Daniel, S.E., Ben-Shlomo, Y., and Lees, A.J. (2002). The accuracy of diagnosis of parkinsonian syndromes in a specialist movement disorder service. Brain 125, 861–870.10.1093/brain/awf080Search in Google Scholar

Ibrahim, N., Kusmirek, J., Struck, A.F., Floberg, J.M., Perlman, S.B., Gallagher, C., and Hall, L.T. (2016). The sensitivity and specificity of F-DOPA PET in a movement disorder clinic. Am. J. Nucl. Med. Mol. Imaging 6, 102–109.Search in Google Scholar

Jellinger, K.A. (1991). Pathology of Parkinson’s disease. Changes other than the nigrostriatal pathway. Mol. Chem. Neuropathol. 14, 153–197.10.1007/BF03159935Search in Google Scholar

Jones, C.M. and Athanasiou, T. (2005). Summary receiver operating characteristic curve analysis techniques in the evaluation of diagnostic tests. Ann. Thorac. Surg. 79, 16–20.10.1016/j.athoracsur.2004.09.040Search in Google Scholar

Juh, R., Kim, J., Moon, D., Choe, B., and Suh, T. (2004). Different metabolic patterns analysis of Parkinsonism on the 18F-FDG PET. Eur. J. Radiol. 51, 223–233.10.1016/S0720-048X(03)00214-6Search in Google Scholar

Kwon, K.Y., Choi, C.G., Kim, J.S., Lee, M.C., and Chung, S.J. (2007). Comparison of brain MRI and 18F-FDG PET in the differential diagnosis of multiple system atrophy from Parkinson’s disease. Mov. Disord. 22, 2352–2358.10.1002/mds.21714Search in Google Scholar PubMed

Kwon, K.Y., Choi, C.G., Kim, J.S., Lee, M.C., and Chung, S.J. (2008). Diagnostic value of brain MRI and 18F-FDG PET in the differentiation of Parkinsonian-type multiple system atrophy from Parkinson’s disease. Eur. J. Neurol. 15, 1043–1049.10.1111/j.1468-1331.2008.02235.xSearch in Google Scholar PubMed

Leeflang, M.M., Deeks, J.J., Gatsonis, C., Bossuyt, P.M., and Cochrane Diagnostic Test Accuracy Working Group. (2008). Systematic reviews of diagnostic test accuracy. Ann. Intern. Med. 149, 889–897.10.7326/0003-4819-149-12-200812160-00008Search in Google Scholar PubMed PubMed Central

Meles, S.K., Tang, C.C., Teune, L.K., Dierckx, R.A., Dhawan, V., Mattis, P.J., Leenders, K.L., and Eidelberg, D. (2015). Abnormal metabolic pattern associated with cognitive impairment in Parkinson’s disease: a validation study. J. Cereb. Blood Flow Metab. 35, 1478–1484.10.1038/jcbfm.2015.112Search in Google Scholar PubMed PubMed Central

Meles, S.K., Renken, R.J., Janzen, A., Vadasz, D., Pagani, M., Arnaldi, D., Morbelli, S., Nobili, F., Mayer, G., Leenders, K.L., et al. (2018). The metabolic pattern of idiopathic REM sleep behavior disorder reflects early-stage Parkinson’s disease. J. Nucl. Med. 59, 1437–1444.10.2967/jnumed.117.202242Search in Google Scholar PubMed

Mudali, D., Teune, L.K., Renken, R.J., Leenders, K.L., and Roerdink, J.B. (2015). Classification of Parkinsonian syndromes from FDG-PET brain data using decision trees with SSM/PCA features. Comput. Math. Methods Med. 2015, 136921.10.1155/2015/136921Search in Google Scholar

Parent, A. and Hazrati, L.N. (1995). Functional anatomy of the basal ganglia. I. The cortico-basal ganglia-thalamo-cortical loop. Brain Res. Brain Res. Rev. 20, 91–127.10.1016/0165-0173(94)00007-CSearch in Google Scholar

Peng, S., Ma, Y., Spetsieris, P.G., Mattis, P., Feigin, A., Dhawan, V., and Eidelberg, D. (2014). Characterization of disease-related covariance topographies with SSMPCA toolbox: effects of spatial normalization and PET scanners. Hum. Brain Mapp. 35, 1801–1814.10.1002/hbm.22295Search in Google Scholar

Petersson, K.M., Nichols, T.E., Poline, J.B., and Holmes, A.P. (1999). Statistical limitations in functional neuroimaging. II. Signal detection and statistical inference. Philos. Trans. R. Soc. Lond. B Biol. Sci. 354, 1261–1281.10.1098/rstb.1999.0478Search in Google Scholar

Reiter, E., Mueller, C., Pinter, B., Krismer, F., Scherfler, C., Esterhammer, R., Kremser, C., Schocke, M., Wenning, G.K., Poewe, W., et al. (2015). Dorsolateral nigral hyperintensity on 3.0T susceptibility-weighted imaging in neurodegenerative parkinsonism. Mov. Disord. 30, 1068–1076.10.1002/mds.26171Search in Google Scholar

Rutter, C.M. and Gatsonis, C.A. (2001). A hierarchical regression approach to meta-analysis of diagnostic test accuracy evaluations. Stat. Med. 20, 2865–2884.10.1002/sim.942Search in Google Scholar

Schindlbeck, K.A. and Eidelberg, D. (2018). Network imaging biomarkers: insights and clinical applications in Parkinson’s disease. Lancet Neurol. 17, 629–640.10.1016/S1474-4422(18)30169-8Search in Google Scholar

Sousa, M.R. and Ribeiro, A.L. (2009). Systematic review and meta-analysis of diagnostic and prognostic studies: a tutorial. Arq. Bras. Cardiol. 92, 229–238, 235–245.Search in Google Scholar

Spetsieris, P.G. and Eidelberg, D. (2011). Scaled subprofile modeling of resting state imaging data in Parkinson’s disease: methodological issues. Neuroimage 54, 2899–2914.10.1016/j.neuroimage.2010.10.025Search in Google Scholar

Tang, C.C., Poston, K.L., Eckert, T., Feigin, A., Frucht, S., Gudesblatt, M., Dhawan, V., Lesser, M., Vonsattel, J.P., Fahn, S., et al. (2010). Differential diagnosis of parkinsonism: a metabolic imaging study using pattern analysis. Lancet Neurol. 9, 149–158.10.1016/S1474-4422(10)70002-8Search in Google Scholar

Taniwaki, T., Nakagawa, M., Yamada, T., Yoshida, T., Ohyagi, Y., Sasaki, M., Kuwabara, Y., Tobimatsu, S., and Kira, J. (2002). Cerebral metabolic changes in early multiple system atrophy: a PET study. J. Neurol. Sci. 200, 79–84.10.1016/S0022-510X(02)00151-XSearch in Google Scholar

Teune, L.K., Bartels, A.L., de Jong, B.M., Willemsen, A.T., Eshuis, S.A., de Vries, J.J., van Oostrom, J.C., and Leenders, K.L. (2010). Typical cerebral metabolic patterns in neurodegenerative brain diseases. Mov. Disord. 25, 2395–2404.10.1002/mds.23291Search in Google Scholar PubMed

Teune, L.K., Renken, R.J., Mudali, D., De Jong, B.M., Dierckx, R.A., Roerdink, J.B., and Leenders, K.L. (2013). Validation of parkinsonian disease-related metabolic brain patterns. Mov. Disord. 28, 547–551.10.1002/mds.25361Search in Google Scholar PubMed

Tomse, P., Jensterle, L., Grmek, M., Zaletel, K., Pirtosek, Z., Dhawan, V., Peng, S., Eidelberg, D., Ma, Y., and Trost, M. (2017a). Abnormal metabolic brain network associated with Parkinson’s disease: replication on a new European sample. Neuroradiology 59, 507–515.10.1007/s00234-017-1821-3Search in Google Scholar PubMed

Tomse, P., Jensterle, L., Rep, S., Grmek, M., Zaletel, K., Eidelberg, D., Dhawan, V., Ma, Y., and Trost, M. (2017b). The effect of 18F-FDG-PET image reconstruction algorithms on the expression of characteristic metabolic brain network in Parkinson’s disease. Phys. Med. 41, 129–135.10.1016/j.ejmp.2017.01.018Search in Google Scholar PubMed PubMed Central

Tripathi, M., Dhawan, V., Peng, S., Kushwaha, S., Batla, A., Jaimini, A., D’Souza, M.M., Sharma, R., Saw, S., and Mondal, A. (2013). Differential diagnosis of parkinsonian syndromes using F-18 fluorodeoxyglucose positron emission tomography. Neuroradiology 55, 483–492.10.1007/s00234-012-1132-7Search in Google Scholar PubMed

Tripathi, M., Tang, C.C., Feigin, A., De Lucia, I., Nazem, A., Dhawan, V., and Eidelberg, D. (2016). Automated differential diagnosis of early parkinsonism using metabolic brain networks: a validation study. J. Nucl. Med. 57, 60–66.10.2967/jnumed.115.161992Search in Google Scholar PubMed

Wang, J., Hoekstra, J.G., Zuo, C., Cook, T.J., and Zhang, J. (2013). Biomarkers of Parkinson’s disease: current status and future perspectives. Drug Discov. Today 18, 155–162.10.1016/j.drudis.2012.09.001Search in Google Scholar PubMed PubMed Central

Whiting, P.F., Rutjes, A.W., Westwood, M.E., Mallett, S., Deeks, J.J., Reitsma, J.B., Leeflang, M.M., Sterne, J.A., Bossuyt, P.M., and QUADAS-2 Group. (2011). QUADAS-2: a revised tool for the Quality Assessment of Diagnostic Accuracy Studies. Ann. Intern. Med. 155, 529–536.10.7326/0003-4819-155-8-201110180-00009Search in Google Scholar PubMed

Wu, P., Wang, J., Peng, S., Ma, Y., Zhang, H., Guan, Y., and Zuo, C. (2013). Metabolic brain network in the Chinese patients with Parkinson’s disease based on 18F-FDG PET imaging. Parkinsonism Relat. Disord. 19, 622–627.10.1016/j.parkreldis.2013.02.013Search in Google Scholar PubMed

Wu, P., Yu, H., Peng, S., Dauvilliers, Y., Wang, J., Ge, J., Zhang, H., Eidelberg, D., Ma, Y., and Zuo, C. (2014). Consistent abnormalities in metabolic network activity in idiopathic rapid eye movement sleep behaviour disorder. Brain 137, 3122–3128.10.1093/brain/awu290Search in Google Scholar PubMed PubMed Central

Received: 2018-06-18
Accepted: 2018-12-28
Published Online: 2019-05-03
Published in Print: 2019-10-25

©2019 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 28.3.2024 from https://www.degruyter.com/document/doi/10.1515/revneuro-2018-0061/html
Scroll to top button