Skip to main content

Advertisement

SpringerLink
Log in

Integration of dynamic parameters in the analysis of 18F-FDopa PET imaging improves the prediction of molecular features of gliomas

  • Original Article
  • Published:
European Journal of Nuclear Medicine and Molecular Imaging Aims and scope Submit manuscript

Abstract

Purpose

18F-FDopa PET imaging of gliomas is routinely interpreted with standardized uptake value (SUV)-derived indices. This study aimed to determine the added value of dynamic 18F-FDopa PET parameters for predicting the molecular features of newly diagnosed gliomas.

Methods

We retrospectively included 58 patients having undergone an 18F-FDopa PET for establishing the initial diagnosis of gliomas, whose molecular features were additionally characterized according to the WHO 2016 classification. Dynamic parameters, involving time-to-peak (TTP) values and curve slopes, were tested for the prediction of glioma types in addition to current static parameters, i.e., tumor-to-normal brain or tumor-to-striatum SUV ratios and metabolic tumor volume (MTV).

Results

There were 21 IDH mutant without 1p/19q co-deletion (IDH+/1p19q−) gliomas, 16 IDH mutants with 1p/19q co-deletion (IDH+/1p19q+) gliomas, and 21 IDH wildtype (IDH−) gliomas. Dynamic parameters enabled differentiating the gliomas according to these molecular features, whereas static parameters did not. In particular, a longer TTP was the single best independent predictor for identifying (1) IDH mutation status (area under the curve (AUC) of 0.789, global accuracy of 74% for the criterion of a TTP ≥ 5.4 min) and (2) 1p/19q co-deletion status (AUC of 0.679, global accuracy of 69% for the criterion of a TTP ≥ 6.9 min). Moreover, the TTP from IDH− gliomas was significantly shorter than those from both IDH+/1p19q− and IDH+/1p19q+ (p ≤ 0.007).

Conclusion

Prediction of the molecular features of newly diagnosed gliomas with 18F-FDopa PET and especially of the presence or not of an IDH mutation, may be obtained with dynamic but not with current static uptake parameters.

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

Similar content being viewed by others

References

  1. Langen K-J, Galldiks N, Hattingen E, Shah NJ. Advances in neuro-oncology imaging. Nat Rev Neurol. 2017;13:279.

    Article  Google Scholar 

  2. Langen K-J, Galldiks N. Update on amino acid PET of brain tumours. Curr Opin Neurol. 2018;31:354–61.

    Article  CAS  Google Scholar 

  3. Galldiks N, Langen K-J. Amino acid PET in neuro-oncology: applications in the clinic. Expert Rev Anticancer Ther. 2017;17:395–7.

    Article  CAS  Google Scholar 

  4. Verger A, Langen KJ. PET Imaging in Glioblastoma: use in clinical practice. In: De Vleeschouwer S, editor. Glioblastoma. Brisbane (AU): Codon Publications; 2017. pp. 155–174.

  5. Verger A, Arbizu J, Law I. Role of amino-acid PET in high-grade gliomas: limitations and perspectives. Q J Nucl Med Mol Imaging. 2018;62:254–66.

    Article  Google Scholar 

  6. Albert NL, Weller M, Suchorska B, Galldiks N, Soffietti R, Kim MM, et al. 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. 2016;18:1199–208.

    Article  CAS  Google Scholar 

  7. Chen W, Silverman DHS, Delaloye S, Czernin J, Kamdar N, Pope W, et al. 18F-FDOPA PET imaging of brain tumors: comparison study with 18F-FDG PET and evaluation of diagnostic accuracy. J Nucl Med. 2006;47:904–11.

    CAS  PubMed  Google Scholar 

  8. Fueger BJ, Czernin J, Cloughesy T, Silverman DH, Geist CL, Walter MA, et al. Correlation of 6-18F-Fluoro-L-Dopa PET uptake with proliferation and tumor grade in newly diagnosed and recurrent gliomas. J Nucl Med. 2010;51:1532–8.

    Article  Google Scholar 

  9. Janvier L, Olivier P, Blonski M, Morel O, Vignaud J-M, Karcher G, et al. Correlation of SUV-derived indices with tumoral aggressiveness of gliomas in static 18F-FDOPA PET: use in clinical practice. Clin Nucl Med. 2015;40:e429–35.

    Article  Google Scholar 

  10. Humbert O, Bourg V, Mondot L, Gal J, Bondiau P-Y, Fontaine D, et al. 18F-DOPA PET/CT in brain tumors: impact on multidisciplinary brain tumor board decisions. Eur J Nucl Med Mol Imaging. 2019;46:558–68.

    Article  Google Scholar 

  11. Bund C, Heimburger C, Imperiale A, Lhermitte B, Chenard M-P, Lefebvre F, et al. FDOPA PET-CT of nonenhancing brain tumors. Clin Nucl Med. 2017;42:250–7.

    Article  Google Scholar 

  12. Patel CB, Fazzari E, Chakhoyan A, Yao J, Raymond C, Nguyen H, et al. 18F-FDOPA PET and MRI characteristics correlate with degree of malignancy and predict survival in treatment-naïve gliomas: a cross-sectional study. J Neuro-Oncol. 2018;139:399–409.

    Article  CAS  Google Scholar 

  13. Schwarzenberg J, Czernin J, Cloughesy TF, Ellingson BM, Pope WB, Grogan T, et al. Treatment response evaluation using 18F-FDOPA PET in patients with recurrent malignant glioma on bevacizumab therapy. Clin Cancer Res. 2014;20:3550–9.

    Article  CAS  Google Scholar 

  14. Pöpperl G, Kreth FW, Mehrkens JH, Herms J, Seelos K, Koch W, et al. FET PET for the evaluation of untreated gliomas: correlation of FET uptake and uptake kinetics with tumour grading. Eur J Nucl Med Mol Imaging. 2007;34:1933–42.

    Article  Google Scholar 

  15. Lohmann P, Herzog H, Rota Kops E, Stoffels G, Judov N, Filss C, et al. Dual-time-point O-(2-[18F]fluoroethyl)-L-tyrosine PET for grading of cerebral gliomas. Eur Radiol. 2015;25:3017–24.

    Article  Google Scholar 

  16. Schiepers C, Chen W, Cloughesy T, Dahlbom M, Huang S-C. 18F-FDOPA kinetics in brain tumors. J Nucl Med. 2007;48:1651–61.

    Article  Google Scholar 

  17. Nioche C, Soret M, Gontier E, Lahutte M, Dutertre G, Dulou R, et al. Evaluation of quantitative criteria for glioma grading with static and dynamic 18F-FDopa PET/CT. Clin Nucl Med. 2013;38:81–7.

    Article  Google Scholar 

  18. Kratochwil C, Combs SE, Leotta K, Afshar-Oromieh A, Rieken S, Debus J, et al. Intra-individual comparison of 18F-FET and 18F-DOPA in PET imaging of recurrent brain tumors. Neuro-Oncol. 2014;16:434–40.

    Article  CAS  Google Scholar 

  19. Louis DN, Perry A, Reifenberger G, von Deimling A, Figarella-Branger D, Cavenee WK, et al. The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol (Berl). 2016;131:803–20.

    Article  Google Scholar 

  20. Lopci E, Riva M, Olivari L, Raneri F, Soffietti R, Piccardo A, et al. Prognostic value of molecular and imaging biomarkers in patients with supratentorial glioma. Eur J Nucl Med Mol Imaging. 2017;44:1155–64.

    Article  CAS  Google Scholar 

  21. Verger A, Stoffels G, Bauer EK, Lohmann P, Blau T, Fink GR, et al. Static and dynamic 18F–FET PET for the characterization of gliomas defined by IDH and 1p/19q status. Eur J Nucl Med Mol Imaging. 2018;45:443–51.

    Article  CAS  Google Scholar 

  22. Kebir S, Weber M, Lazaridis L, Deuschl C, Schmidt T, Mönninghoff C, et al. Hybrid 11C-MET PET/MRI combined with “Machine Learning” in glioma diagnosis according to the revised glioma WHO classification 2016. Clin Nucl Med. 2019;44:214–20.

  23. Suchorska B, Giese A, Biczok A, Unterrainer M, Weller M, Drexler M, et al. Identification of time-to-peak on dynamic 18F-FET-PET as a prognostic marker specifically in IDH1/2 mutant diffuse astrocytoma. Neuro-Oncol. 2018;20:279–88.

    Article  CAS  Google Scholar 

  24. Cicone F, Carideo L, Scaringi C, Arcella A, Giangaspero F, Scopinaro F, et al. 18F-DOPA uptake does not correlate with IDH mutation status and 1p/19q co-deletion in glioma. Ann Nucl Med. 2019;33:295–302.

    Article  CAS  Google Scholar 

  25. Verger A, Metellus P, Sala Q, Colin C, Bialecki E, Taieb D, et al. IDH mutation is paradoxically associated with higher 18F-FDOPA PET uptake in diffuse grade II and grade III gliomas. Eur J Nucl Med Mol Imaging. 2017;44:1306–11.

    Article  CAS  Google Scholar 

  26. Isal S, Gauchotte G, Rech F, Blonski M, Planel S, Chawki MB, et al. A high 18F-FDOPA uptake is associated with a slow growth rate in diffuse Grade II–III gliomas. Br J Radiol. 2018;91(1084):20170803.

  27. Hoffman JM, Melega WP, Hawk TC, Grafton SC, Luxen A, Mahoney DK, et al. The effects of carbidopa administration on 6-[18F]fluoro-L-dopa kinetics in positron emission tomography. J Nucl Med. 1992;33:1472–7.

    CAS  PubMed  Google Scholar 

  28. Law I, Albert NL, Arbizu J, Boellaard R, Drzezga A, Galldiks N, et al. 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. 2019;46:540–57.

    Article  CAS  Google Scholar 

  29. Ye H, Wong K-P, Wardak M, Dahlbom M, Kepe V, Barrio JR, et al. Automated movement correction for dynamic PET/CT images: evaluation with phantom and patient data. Chen K, editor. PLoS ONE. 2014;9:e103745.

    Article  Google Scholar 

  30. Ceccon G, Lohmann P, Stoffels G, Judov N, Filss CP, Rapp M, et al. Dynamic O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography differentiates brain metastasis recurrence from radiation injury after radiotherapy. Neuro-Oncol. 2017;19:281–8.

  31. Perizzolo M, Winkfein B, Hui S, Krulicki W, Chan JA, Demetrick DJ. IDH mutation detection in formalin-fixed paraffin-embedded gliomas using multiplex PCR and single-base extension: IDH1/2 mutation detection by SNaPshot®. Brain Pathol. 2012;22:619–24.

    Article  CAS  Google Scholar 

  32. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, et al. The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol (Berl). 2007;114:97–109.

    Article  Google Scholar 

  33. The Cancer Genome Atlas Research Network. Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. N Engl J Med. 2015;372:2481–98.

    Article  Google Scholar 

  34. Pallud J, Blonski M, Mandonnet E, Audureau E, Fontaine D, Sanai N, et al. Velocity of tumor spontaneous expansion predicts long-term outcomes for diffuse low-grade gliomas. Neuro-Oncol. 2013;15:595–606.

    Article  Google Scholar 

  35. Kaloshi G, Psimaras D, Mokhtari K, Dehais C, Houillier C, Marie Y, et al. Supratentorial low-grade gliomas in older patients. Neurology. 2009;73:2093–8.

    Article  CAS  Google Scholar 

  36. Jansen NL, Schwartz C, Graute V, Eigenbrod S, Lutz J, Egensperger R, et al. Prediction of oligodendroglial histology and LOH 1p/19q using dynamic [18F]FET-PET imaging in intracranial WHO grade II and III gliomas. Neuro-Oncol. 2012;14:1473–80.

    Article  CAS  Google Scholar 

  37. Manabe O, Hattori N, Yamaguchi S, Hirata K, Kobayashi K, Terasaka S, et al. Oligodendroglial component complicates the prediction of tumour grading with metabolic imaging. Eur J Nucl Med Mol Imaging. 2015;42:896–904.

    Article  CAS  Google Scholar 

  38. Bashir A, Brennum J, Broholm H, Law I. The diagnostic accuracy of detecting malignant transformation of low-grade glioma using O-(2-[18F]fluoroethyl)-l-tyrosine positron emission tomography: a retrospective study. J Neurosurg. 2018;130:451–64.

  39. Nomura Y, Asano Y, Shinoda J, Yano H, Ikegame Y, Kawasaki T, et al. Characteristics of time-activity curves obtained from dynamic 11C-methionine PET in common primary brain tumors. J Neuro-Oncol. 2018;138:649–58.

    Article  CAS  Google Scholar 

  40. Verger A, Taieb D, Guedj E. Is the information provided by amino acid PET radiopharmaceuticals clinically equivalent in gliomas? Eur J Nucl Med Mol Imaging. 2017;44:1408–10.

    Article  CAS  Google Scholar 

  41. Dadone-Montaudié B, Ambrosetti D, Dufour M, Darcourt J, Almairac F, Coyne J, et al. [18F] FDOPA standardized uptake values of brain tumors are not exclusively dependent on LAT1 expression. PLoS One. 2017;12:e0184625.

    Article  Google Scholar 

  42. Pöpperl G, Kreth FW, Herms J, Koch W, Mehrkens JH, Gildehaus FJ, et al. Analysis of 18F-FET PET for grading of recurrent gliomas: is evaluation of uptake kinetics superior to standard methods? J Nucl Med. 2006;47:393–403.

    PubMed  Google Scholar 

  43. Jiang H, Cui Y, Wang J, Lin S. Impact of epidemiological characteristics of supratentorial gliomas in adults brought about by the 2016 world health organization classification of tumors of the central nervous system. Oncotarget. 2017;8:20354–61.

    PubMed  Google Scholar 

  44. Jansen NL, Suchorska B, Wenter V, Schmid-Tannwald C, Todica A, Eigenbrod S, et al. Prognostic significance of dynamic 18F-FET PET in newly diagnosed astrocytic high-grade glioma. J Nucl Med. 2015;56:9–15.

    Article  CAS  Google Scholar 

  45. Unterrainer M, Schweisthal F, Suchorska B, Wenter V, Schmid-Tannwald C, Fendler WP, et al. Serial 18F-FET PET imaging of primarily 18F-FET-negative glioma: does it make sense? J Nucl Med. 2016;57:1177–82.

    Article  CAS  Google Scholar 

Download references

Acknowledgments

The authors thank Pierre Pothier for his critical review of the manuscript.

Author information

Author notes

  1. Merwan Ginet and Timothée Zaragori contributed equally to this work.

Authors and Affiliations

  1. CHRU-Nancy, Department of Nuclear Medicine & Nancyclotep Imaging platform, Université de Lorraine, F-54000, Nancy, France

    Merwan Ginet, Timothée Zaragori, Pierre-Yves Marie, Véronique Roch, Laëtitia Imbert & Antoine Verger

  2. IADI, INSERM, UMR 1254, Université de Lorraine, F-54000, Nancy, France

    Timothée Zaragori, Laëtitia Imbert & Antoine Verger

  3. Université de Lorraine, INSERM U1116, F-54000, Nancy, France

    Pierre-Yves Marie & Zohra Lamiral

  4. CHRU-Nancy, Department of Pathology, Université de Lorraine, F-54000, Nancy, France

    Guillaume Gauchotte

  5. INSERM U1256, Université de Lorraine, F-54000, Nancy, France

    Guillaume Gauchotte

  6. Department of Neurosurgery, CHU-Nancy, F-54000, Nancy, France

    Fabien Rech & Marie Blonski

  7. Centre de Recherche en Automatique de Nancy CRAN, CNRS UMR 7039, Université de Lorraine, F-54000, Nancy, France

    Fabien Rech, Marie Blonski & Luc Taillandier

  8. CHRU-Nancy, Department of Neuro-oncology, Université de Lorraine, F-54000, Nancy, France

    Luc Taillandier

Authors
  1. Merwan Ginet
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Timothée Zaragori
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pierre-Yves Marie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Véronique Roch
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Guillaume Gauchotte
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fabien Rech
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Marie Blonski
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Zohra Lamiral
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Luc Taillandier
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Laëtitia Imbert
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Antoine Verger
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Antoine Verger.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed written consent was obtained from all individual participants included in the study.

Additional information

Publisher’s note

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

This article is part of the Topical Collection on Oncology – Brain

Electronic supplementary material

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ginet, M., Zaragori, T., Marie, PY. et al. Integration of dynamic parameters in the analysis of 18F-FDopa PET imaging improves the prediction of molecular features of gliomas. Eur J Nucl Med Mol Imaging 47, 1381–1390 (2020). https://doi.org/10.1007/s00259-019-04509-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00259-019-04509-y

Keywords

Search

Navigation