Skip to main content
SpringerLink
Log in

Static 18F-FET PET and DSC-PWI based on hybrid PET/MR for the prediction of gliomas defined by IDH and 1p/19q status

  • Neuro
  • Published:
European Radiology Aims and scope Submit manuscript

Abstract

Objectives

To investigate the predictive value of static O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography (18F-FET PET) and cerebral blood volume (CBV) for glioma grading and determining isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status.

Methods

Fifty-two patients with newly diagnosed gliomas who underwent simultaneous 18F-FET PET and dynamic susceptibility contrast perfusion-weighted imaging (DSC-PWI) examinations on hybrid PET/MR were retrospectively enrolled. The mean and max tumor-to-brain ratio (TBR) and normalized CBV (nCBV) were calculated based on whole tumor volume segmentations with reference to PET/MR images. The predictive efficacy of FET PET and CBV in glioma according to the 2016 World Health Organization (WHO) classification was evaluated by receiver operating characteristic curve analyses with the area under the curve (AUC).

Results

TBRmean, TBRmax, nCBVmean, and nCBVmax differed between low- and high-grade gliomas, with the highest AUC of nCBVmean (0.920). TBRmax and nCBVmean showed significant differences between gliomas with and without IDH mutation (p = 0.032 and 0.010, respectively). Furthermore, TBRmean, TBRmax, and nCBVmean discriminated between IDH-wildtype glioblastomas and IDH-mutated astrocytomas (p = 0.049, 0.034 and 0.029, respectively). The combination of TBRmax and nCBVmean showed the best predictive performance (AUC, 0.903). Only nCBVmean differentiated IDH-mutated with 1p/19q codeletion oligodendrogliomas from IDH-wildtype glioblastomas (p < 0.001) (AUC, 0.829), but none of the parameters discriminated between oligodendrogliomas and astrocytomas.

Conclusions

Both FET PET and DSC-PWI might be non-invasive predictors for glioma grades and IDH mutation status. FET PET combined with CBV could improve the differentiation of IDH-mutated astrocytomas and IDH-wildtype glioblastomas. However, FET PET and CBV might be limited for identifying oligodendrogliomas.

Key Points

• Static 18F-FET PET and DSC-PWI parameters differed between low- and high-grade gliomas, with the highest AUC of the mean value of normalized CBV.

• Static 18F-FET PET and DSC-PWI parameters based on hybrid PET/MR showed predictive value in identifying glioma IDH mutation subtypes, which have gained importance for both determining the diagnosis and prognosis of gliomas according to the 2016 WHO classification.

• Static 18F-FET PET and DSC-PWI parameters have limited potential in differentiating IDH-mutated with 1p/19q codeletion oligodendrogliomas from IDH-wildtype glioblastomas or IDH-mutated astrocytomas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Abbreviations

18F-FET PET:

O-(2-18F-Fluoroethyl)-L-tyrosine positron emission tomography

AUC:

Area under the curve

DSC-PWI:

Dynamic susceptibility contrast perfusion-weighted imaging

IDH:

Isocitrate dehydrogenase

nCBV:

Normalized cerebral blood volume

ROC:

Receiver operating characteristic

TBR:

Tumor-to-brain ratio

VOI:

Volume of interest

WHO:

World Health Organization

References

  1. Rogers TW, Toor G, Drummond K et al (2018) The 2016 revision of the WHO Classification of Central Nervous System Tumours: retrospective application to a cohort of diffuse gliomas. J Neurooncol 137:181–189. https://doi.org/10.1007/s11060-017-2710-7

    Article  PubMed  Google Scholar 

  2. Eckel-Passow JE, Lachance DH, Molinaro AM et al (2015) Glioma groups based on 1p/19q, IDH, and TERT promoter mutations in tumors. N Engl J Med 372:2499–2508. https://doi.org/10.1056/NEJMoa1407279

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Kloosterhof NK, Bralten LB, Dubbink HJ, French PJ, van den Bent MJ (2011) Isocitrate dehydrogenase-1 mutations: a fundamentally new understanding of diffuse glioma? Lancet Oncol 12:83–91. https://doi.org/10.1016/S1470-2045(10)70053-X

    Article  CAS  PubMed  Google Scholar 

  4. Buckner J, Giannini C, Eckel-Passow J et al (2017) Management of diffuse low-grade gliomas in adults - use of molecular diagnostics. Nat Rev Neurol 13:340–351. https://doi.org/10.1038/nrneurol.2017.54

    Article  CAS  PubMed  Google Scholar 

  5. Ostrom QT, Cote DJ, Ascha M, Kruchko C, Barnholtz-Sloan JS (2018) Adult glioma incidence and survival by race or ethnicity in the United States from 2000 to 2014. JAMA Oncol 4:1254–1262. https://doi.org/10.1001/jamaoncol.2018.1789

    Article  PubMed  PubMed Central  Google Scholar 

  6. Broen MPG, Smits M, Wijnenga MMJ et al (2018) The T2-FLAIR mismatch sign as an imaging marker for non-enhancing IDH-mutant, 1p/19q-intact lower-grade glioma: a validation study. Neuro Oncol 20:1393–1399. https://doi.org/10.1093/neuonc/noy048

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Smits M, van den Bent MJ (2017) Imaging correlates of adult glioma genotypes. Radiology 284:316–331. https://doi.org/10.1148/radiol.2017151930

    Article  PubMed  Google Scholar 

  8. Englander ZK, Horenstein CI, Bowden SG et al (2018) Extent of bold vascular dysregulation is greater in diffuse gliomas without isocitrate dehydrogenase 1 R132H mutation. Radiology 287:965–972. https://doi.org/10.1148/radiol.2017170790

    Article  PubMed  Google Scholar 

  9. Latysheva A, Emblem KE, Brandal P et al (2019) Dynamic susceptibility contrast and diffusion MR imaging identify oligodendroglioma as defined by the 2016 WHO classification for brain tumors: histogram analysis approach. Neuroradiology 61:545–555. https://doi.org/10.1007/s00234-019-02173-5

    Article  PubMed  Google Scholar 

  10. Hyun SC, Sung KH, Chai JS et al (2019) Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis. Eur Radiol 29:745–758. https://doi.org/10.1007/s00330-018-5608-7

    Article  Google Scholar 

  11. Albert NL, Weller M, Suchorska B et al (2016) Response Assessment in Neuro-Oncology working group and European Association for Neuro-Oncology recommendations for the clinical use of PET imaging in gliomas. Neuro Oncol 18:1199–1208. https://doi.org/10.1093/neuonc/now058

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Law I, Albert NL, Arbizu J et al (2018) Joint EANM/EANO/RANO practice guidelines/SNMMI procedure standards for imaging of gliomas using PET with radiolabelled amino acids and [18F]FDG: version 1.0. Eur J Nucl Med Mol Imaging 46:540–557. https://doi.org/10.1007/s00259-018-4207-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Galldiks N, Stoffels G, Ruge MI et al (2013) Role of O-(2-18F-fluoroethyl)-L-tyrosine PET as a diagnostic tool for detection of malignant progression in patients with low-grade glioma. J Nucl Med 54:2046–2054. https://doi.org/10.2967/jnumed.113.123836

    Article  CAS  PubMed  Google Scholar 

  14. Kunz M, Thon N, Eigenbrod S et al (2011) Hot spots in dynamic (FET)-F-18-PET delineate malignant tumor parts within suspected WHO grade II gliomas. Neuro Oncol 13:307–316. https://doi.org/10.1093/neuonc/noq196

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Verger A, Stoffels G, Bauer EK et al (2018) Static and dynamic 18F–FET PET for the characterization of gliomas defined by IDH and 1p/19q status. Eur J Nucl Med Mol Imaging 45:443–451. https://doi.org/10.1007/s00259-017-3846-6

    Article  CAS  PubMed  Google Scholar 

  16. Vettermann F, Suchorska B, Unterrainer M et al (2019) Non-invasive prediction of IDH-wildtype genotype in gliomas using dynamic 18F-FET PET. Eur J Nucl Med Mol Imaging 46:2581–2589. https://doi.org/10.1007/s00259-019-04477-3

    Article  CAS  PubMed  Google Scholar 

  17. Song S, Cheng Y, Ma J et al (2020) Simultaneous FET-PET and contrast-enhanced MRI based on hybrid PET/MR improves delineation of tumor spatial biodistribution in gliomas: a biopsy validation study. Eur J Nucl Med Mol Imaging 47:1458–1467. https://doi.org/10.1007/s00259-019-04656-2

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Welker K, Boxerman J, Kalnin A et al (2015) ASFNR recommendations for clinical performance of MR dynamic susceptibility contrast perfusion imaging of the brain. AJNR Am J Neuroradiol 36:E41–E51. https://doi.org/10.3174/ajnr.A4341

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Munck Af Rosenschold P, Costa J, Engelholm SA et al (2015) Impact of [18F]-fluoro-ethyl-tyrosine PET imaging on target definition for radiation therapy of high-grade glioma. Neuro Oncol 17:757–763. https://doi.org/10.1093/neuonc/nou316

    Article  CAS  PubMed  Google Scholar 

  20. Pauleit D, Floeth F, Hamacher K et al (2005) O-(2-[18F]fluoroethyl)-L-tyrosine PET combined with MRI improves the diagnostic assessment of cerebral gliomas. Brain 128:678–687. https://doi.org/10.1093/brain/awh399

    Article  PubMed  Google Scholar 

  21. Lohmann P, Stavrinou P, Lipke K et al (2019) FET PET reveals considerable spatial differences in tumour burden compared to conventional MRI in newly diagnosed glioblastoma. Eur J Nucl Med Mol Imaging 46:591–602. https://doi.org/10.1007/s00259-018-4188-8

    Article  CAS  PubMed  Google Scholar 

  22. Filss CP, Galldiks N, Stoffels G et al (2014) Comparison of 18F-FET PET and perfusion-weighted MR imaging: a PET/MR imaging hybrid study in patients with brain tumors. J Nucl Med 55:540–545. https://doi.org/10.2967/jnumed.113.129007

    Article  CAS  PubMed  Google Scholar 

  23. Langen KJ, Watts C (2016) Neuro-oncology: amino acid PET for brain tumours - ready for the clinic? Nat Rev Neurol 12:375–376. https://doi.org/10.1038/nrneurol.2016.80

    Article  PubMed  Google Scholar 

  24. Pöpperl G, Kreth FW, Mehrkens JH et al (2007) FET PET for the evaluation of untreated gliomas: correlation of FET uptake and uptake kinetics with tumour grading. Eur J Nucl Med Mol Imaging 34:1933–1942. https://doi.org/10.1007/s00259-007-0534-y

    Article  PubMed  Google Scholar 

  25. Rapp M, Heinzel A, Galldiks N et al (2013) Diagnostic performance of F-18-FET PET in newly diagnosed cerebral lesions suggestive of glioma. J Nucl Med 54:229–235. https://doi.org/10.2967/jnumed.112.109603

    Article  CAS  PubMed  Google Scholar 

  26. Unterrainer M, Vettermann F, Brendel M et al (2017) Towards standardization of 18F-FET PET imaging: do we need a consistent method of background activity assessment? EJNMMI Res 7:48. https://doi.org/10.1186/s13550-017-0295-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kebir S, Weber M, Lazaridis L et al (2019) Hybrid C-11-MET PET/MRI combined with “machine learning” in glioma diagnosis according to the revised glioma WHO Classification 2016. Clin Nucl Med 44:214–220. https://doi.org/10.1097/RLU.0000000000002398

    Article  PubMed  Google Scholar 

  28. Liang J, Liu D, Gao P et al (2017) Diagnostic values of DCE-MRI and DSC-MRI for differentiation between high-grade and low-grade gliomas: a comprehensive meta-analysis. Acad Radiol 25:338–348. https://doi.org/10.1016/j.acra.2017.10.001

    Article  PubMed  Google Scholar 

  29. Brendle C, Hempel J, Schittenhelm J et al (2018) Glioma grading by dynamic susceptibility contrast perfusion and (11)C-methionine positron emission tomography using different regions of interest. Neuroradiology 60:381–389. https://doi.org/10.1007/s00234-018-1993-5

    Article  PubMed  Google Scholar 

  30. Anzalone N, Castellano A, Cadioli M et al (2018) Brain gliomas: multicenter standardized assessment of dynamic contrast-enhanced and dynamic susceptibility contrast MR images. Radiology 287:933–943. https://doi.org/10.1148/radiol.2017170362

    Article  PubMed  Google Scholar 

  31. Verger A, Filss CP, Lohmann P et al (2017) Comparison of 18F-FET PET and perfusion-weighted MRI for glioma grading: a hybrid PET/MR study. Eur J Nucl Med Mol Imaging 44:2257–2265. https://doi.org/10.1007/s00259-017-3812-3

    Article  CAS  PubMed  Google Scholar 

  32. Lee JY, Ahn KJ, Lee YS, Jang JH, Jung SL, Kim BS (2018) Differentiation of grade II and III oligodendrogliomas from grade II and III astrocytomas: a histogram analysis of perfusion parameters derived from dynamic contrast-enhanced (DCE) and dynamic susceptibility contrast (DSC) MRI. Acta Radiol 59:723–731. https://doi.org/10.1177/0284185117728981

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

The authors thank Jie Ma, Yu Yang, Dongmei Shuai, and Qingtang Lin for assistance with the patient studies, and Cheng Peng and Zhigang Liang for the radiosynthesis of 18F-FET.

Funding

This study has received funding by Beijing Municipal Administration of Hospitals’ Ascent Plan (DFL20180802) and Beijing Higher Education Young Elite Teacher Project (CN) (CIT&TCD201904091).

Author information

Authors and Affiliations

  1. Department of Radiology, Xuanwu Hospital, Capital Medical University, No. 45 Changchun Street, Xicheng District, Beijing, 100053, China

    Shuangshuang Song & Jie Lu

  2. Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China

    Shuangshuang Song & Jie Lu

  3. Department of Pathology, Xuanwu Hospital, Capital Medical University, Beijing, China

    Leiming Wang

  4. Department of Nuclear Medicine, Xuanwu Hospital, Capital Medical University, Beijing, China

    Hongwei Yang & Jie Lu

  5. Department of Neurosurgery, Xuanwu Hospital, Capital Medical University, Beijing, China

    Yongzhi Shan, Ye Cheng, Lixin Xu & Guoguang Zhao

  6. GE Healthcare, Beijing, China

    Chengyan Dong

Authors
  1. Shuangshuang Song
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Leiming Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Hongwei Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongzhi Shan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ye Cheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Lixin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Chengyan Dong
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Guoguang Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Jie Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Lu.

Ethics declarations

Guarantor

The scientific guarantor of this publication is Jie Lu.

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.

Ethical approval

The Institutional Review Board of Xuanwu Hospital Capital Medical University approval was obtained.

Methodology

• retrospective

• diagnostic or prognostic study

• performed at one institution

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Shuangshuang Song and Leiming Wang contribute equally to this work as co-first author.

Supplementary Information

ESM 1

(DOC 33 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Song, S., Wang, L., Yang, H. et al. Static 18F-FET PET and DSC-PWI based on hybrid PET/MR for the prediction of gliomas defined by IDH and 1p/19q status. Eur Radiol 31, 4087–4096 (2021). https://doi.org/10.1007/s00330-020-07470-9

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00330-020-07470-9

Keywords

Search

Navigation