Journal of Neuro-Oncology

, Volume 138, Issue 3, pp 649–658 | Cite as

Characteristics of time-activity curves obtained from dynamic 11C-methionine PET in common primary brain tumors

  • Yuichi Nomura
  • Yoshitaka Asano
  • Jun Shinoda
  • Hirohito Yano
  • Yuka Ikegame
  • Tomohiro Kawasaki
  • Noriyuki Nakayama
  • Takashi Maruyama
  • Yoshihiro Muragaki
  • Toru Iwama
Clinical Study



The aim of this study was to assess whether dynamic PET with 11C-methionine (MET) (MET-PET) is useful in the diagnosis of brain tumors.


One hundred sixty patients with brain tumors (139 gliomas, 9 meningiomas, 4 hemangioblastomas and 8 primary central nervous system lymphomas [PCNSL]) underwent dynamic MET-PET with a 3-dimensional acquisition mode, and the maximum tumor MET-standardized uptake value (MET-SUV) was measured consecutively to construct a time-activity curve (TAC). Furthermore, receiver operating characteristic (ROC) curves were generated from the time-to-peak (TTP) and the slope of the curve in the late phase (SLOPE).


The TAC patterns of MET-SUVs (MET-TACs) could be divided into four characteristic types when MET dynamics were analyzed by dividing the MET-TAC into three phases. MET-SUVs were significantly higher in early and late phases in glioblastoma compared to anaplastic astrocytoma, diffuse astrocytoma and the normal frontal cortex (P < 0.05). The SLOPE in the late phase was significantly lower in tumors that included an oligodendroglial component compared to astrocytic tumors (P < 0.001). When we set the cutoff of the SLOPE in the late phase to − 0.04 h−1 for the differentiation of tumors that included an oligodendroglial component from astrocytic tumors, the diagnostic accuracy was 74.2% sensitivity and 64.9% specificity. The area under the ROC curve was 0.731.


The results of this study show that quantification of the MET-TAC for each brain tumor identified by a dynamic MET-PET study could be helpful in the non-invasive discrimination of brain tumor subtypes, in particular gliomas.


Methionine-PET Time activity curve Brain tumor Dynamic PET 3D-acquisition mode 



Anaplastic astrocytoma


Anaplastic oligoastrocytoma


Anaplastic oligoastrocytoma


Blood brain barrier






Diffuse astrocytoma




Glioblatoma multiforme




MET-standardized uptake value


Time-activity curve of MET-SUV






Primary central nervous system lymphoma


Region of interest


Regression coefficient of TAC slope


Standardized uptake value


Time activity curve


Tissue blood flow


Tissue blood volume





The authors would like to thank Dr. Kazutoshi Yokoyama, Dr. Takeshi Ito, and Dr. Makoto Okada at the Department of Neurosurgery, Kizawa Memorial Hospital, and Dr. Soko Ikuta at the Department of Neurosurgery, Tokyo Women’s Medical University for referring patients. They would also like to thank Mr. Yukinori Kasuya, Mr. Ryuji Okumura, Mr. Yu-ichi Yamada, and Mr. Seisuke Fukuyama for technical support with MRI/PET scanning.

Compliance with Ethical Standards

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 research committee of Gifu University Hospital and Kizawa Memorial Hospital Foundation and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. This article does not contain any studies with animals performed by any of the authors.

Informed consent

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


  1. 1.
    Di Chiro G, DeLaPaz RL, Brooks RA, Sokoloff L, Kornblith PL, Smith BH, Patronas NJ, Kufta CV, Kessler RM, Johnston GS, Manning RG, Wolf AP (1982) Glucose utilization of cerebral gliomas measured by [18F] fluorodeoxyglucose and positron emission tomography. Neurology 32(12):1323–1329CrossRefPubMedGoogle Scholar
  2. 2.
    Delbeke D, Meyerowitz C, Lapidus RL, Maciunas RJ, Jennings MT, Moots PL, Kessler RM (1995) Optimal cutoff levels of F-18 fluorodeoxyglucose uptake in the differentiation of low-grade from high-grade brain tumors with PET. Radiology 195(1):47–52. CrossRefPubMedGoogle Scholar
  3. 3.
    Ogawa T, Inugami A, Hatazawa J, Kanno I, Murakami M, Yasui N, Mineura K, Uemura K (1996) Clinical positron emission tomography for brain tumors: comparison of fludeoxyglucose F 18 and L-methyl-11C-methionine. Am J Neuroradiol 17(2):345–353PubMedGoogle Scholar
  4. 4.
    Kaschten B, Stevenaert A, Sadzot B, Deprez M, Degueldre C, Del Fiore G, Luxen A, Reznik M (1998) Preoperative evaluation of 54 gliomas by PET with fluorine-18-fluorodeoxyglucose and/or carbon-11-methionine. J Nucl Med 39(5):778–785PubMedGoogle Scholar
  5. 5.
    Herholz K, Holzer T, Bauer B, Schroder R, Voges J, Ernestus RI, Mendoza G, Weber-Luxenburger G, Lottgen J, Thiel A, Wienhard K, Heiss WD (1998) 11C-methionine PET for differential diagnosis of low-grade gliomas. Neurology 50(5):1316–1322CrossRefPubMedGoogle Scholar
  6. 6.
    De Witte O, Goldberg I, Wikler D, Rorive S, Damhaut P, Monclus M, Salmon I, Brotchi J, Goldman S (2001) Positron emission tomography with injection of methionine as a prognostic factor in glioma. J Neurosurg 95(5):746–750. CrossRefPubMedGoogle Scholar
  7. 7.
    Nariai T, Tanaka Y, Wakimoto H, Aoyagi M, Tamaki M, Ishiwata K, Senda M, Ishii K, Hirakawa K, Ohno K (2005) Usefulness of L-[methyl-11C] methionine-positron emission tomography as a biological monitoring tool in the treatment of glioma. J Neurosurg 103(3):498–507. CrossRefPubMedGoogle Scholar
  8. 8.
    Kim S, Chung JK, Im SH, Jeong JM, Lee DS, Kim DG, Jung HW, Lee MC (2005) 11C-methionine PET as a prognostic marker in patients with glioma: comparison with 18F-FDG PET. Eur J Nucl Med Mol Imaging 32(1):52–59. CrossRefPubMedGoogle Scholar
  9. 9.
    Albert NL, Weller M, Suchorska B, Galldiks N, Soffietti R, Kim MM, la Fougere C, Pope W, Law I, Arbizu J, Chamberlain MC, Vogelbaum M, Ellingson BM, Tonn JC (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(9):1199–1208. CrossRefPubMedPubMedCentralGoogle Scholar
  10. 10.
    Ceyssens S, Van Laere K, de Groot T, Goffin J, Bormans G, Mortelmans L (2006) [11C]methionine PET, histopathology, and survival in primary brain tumors and recurrence. Am J Neuroradiol 27(7):1432–1437PubMedGoogle Scholar
  11. 11.
    Kato T, Shinoda J, Oka N, Miwa K, Nakayama N, Yano H, Maruyama T, Muragaki Y, Iwama T (2008) Analysis of 11C-methionine uptake in low-grade gliomas and correlation with proliferative activity. Am J Neuroradiol 29(10):1867–1871. CrossRefPubMedGoogle Scholar
  12. 12.
    Hatakeyama T, Kawai N, Nishiyama Y, Yamamoto Y, Sasakawa Y, Ichikawa T, Tamiya T (2008) 11C-methionine (MET) and 18F-fluorothymidine (FLT) PET in patients with newly diagnosed glioma. Eur J Nucl Med Mol Imaging 35(11):2009–2017. CrossRefPubMedGoogle Scholar
  13. 13.
    Terakawa Y, Tsuyuguchi N, Iwai Y, Yamanaka K, Higashiyama S, Takami T, Ohata K (2008) Diagnostic accuracy of 11C-methionine PET for differentiation of recurrent brain tumors from radiation necrosis after radiotherapy. J Nucl Med 49(5):694–699. CrossRefPubMedGoogle Scholar
  14. 14.
    Aki T, Nakayama N, Yonezawa S, Takenaka S, Miwa K, Asano Y, Shinoda J, Yano H, Iwama T (2012) Evaluation of brain tumors using dynamic 11C-methionine-PET. J Neurooncol 109(1):115–122. CrossRefPubMedGoogle Scholar
  15. 15.
    Calcagni ML, Galli G, Giordano A, Taralli S, Anile C, Niesen A, Baum RP (2011) Dynamic O-(2-[18F]fluoroethyl)-L-tyrosine (F-18 FET) PET for glioma grading: assessment of individual probability of malignancy. Clin Nucl Med 36(10):841–847. CrossRefPubMedGoogle Scholar
  16. 16.
    Kunz M, Thon N, Eigenbrod S, Hartmann C, Egensperger R, Herms J, Geisler J, la Fougere C, Lutz J, Linn J, Kreth S, von Deimling A, Tonn JC, Kretzschmar HA, Popperl G, Kreth FW (2011) Hot spots in dynamic (18)FET-PET delineate malignant tumor parts within suspected WHO grade II gliomas. Neuro Oncol 13(3):307–316. CrossRefPubMedPubMedCentralGoogle Scholar
  17. 17.
    Thon N, Kunz M, Lemke L, Jansen NL, Eigenbrod S, Kreth S, Lutz J, Egensperger R, Giese A, Herms J, Weller M, Kretzschmar H, Tonn JC, la Fougere C, Kreth FW (2015) Dynamic 18F-FET PET in suspected WHO grade II gliomas defines distinct biological subgroups with different clinical courses. Int J Cancer 136(9):2132–2145. CrossRefPubMedGoogle Scholar
  18. 18.
    Galldiks N, Stoffels G, Filss C, Rapp M, Blau T, Tscherpel C, Ceccon G, Dunkl V, Weinzierl M, Stoffel M, Sabel M, Fink GR, Shah NJ, Langen KJ (2015) The use of dynamic O-(2-18F-fluoroethyl)-l-tyrosine PET in the diagnosis of patients with progressive and recurrent glioma. Neuro Oncol 17(9):1293–1300. PubMedPubMedCentralGoogle Scholar
  19. 19.
    Ceccon G, Lohmann P, Stoffels G, Judov N, Filss CP, Rapp M, Bauer E, Hamisch C, Ruge MI, Kocher M, Kuchelmeister K, Sellhaus B, Sabel M, Fink GR, Shah NJ, Langen KJ, Galldiks N (2017) Dynamic O-(2-18F-fluoroethyl)-L-tyrosine positron emission tomography differentiates brain metastasis recurrence from radiation injury after radiotherapy. Neuro Oncol 19(2):281–288. PubMedGoogle Scholar
  20. 20.
    Moulin-Romsee G, D’Hondt E, de Groot T, Goffin J, Sciot R, Mortelmans L, Menten J, Bormans G, Van Laere K (2007) Non-invasive grading of brain tumours using dynamic amino acid PET imaging: does it work for 11C-methionine? Eur J Nucl Med Mol Imaging 34(12):2082–2087. CrossRefPubMedGoogle Scholar
  21. 21.
    Kapouleas I, Alavi A, Alves WM, Gur RE, Weiss DW (1991) Registration of three-dimensional MR and PET images of the human brain without markers. Radiology 181(3):731–739. CrossRefPubMedGoogle Scholar
  22. 22.
    Glaudemans AW, Enting RH, Heesters MA, Dierckx RA, van Rheenen RW, Walenkamp AM, Slart RH (2013) Value of 11C-methionine PET in imaging brain tumours and metastases. Eur J Nucl Med Mol Imaging 40(4):615–635. CrossRefPubMedGoogle Scholar
  23. 23.
    Jansen NL, Schwartz C, Graute V, Eigenbrod S, Lutz J, Egensperger R, Popperl G, Kretzschmar HA, Cumming P, Bartenstein P, Tonn JC, Kreth FW, la Fougere C, Thon N (2012) Prediction of oligodendroglial histology and LOH 1p/19q using dynamic [(18)F]FET-PET imaging in intracranial WHO grade II and III gliomas. Neuro Oncol 14(12):1473–1480. CrossRefPubMedPubMedCentralGoogle Scholar
  24. 24.
    Okada Y, Nihashi T, Fujii M, Kato K, Okochi Y, Ando Y, Yamashita M, Maesawa S, Takebayashi S, Wakabayashi T, Naganawa S (2012) Differentiation of newly diagnosed glioblastoma multiforme and intracranial diffuse large B-cell Lymphoma using (11)C-methionine and (18)F-FDG PET. Clin Nucl Med 37(9):843–849. CrossRefPubMedGoogle Scholar
  25. 25.
    Kanai Y, Segawa H, Miyamoto K, Uchino H, Takeda E, Endou H (1998) Expression cloning and characterization of a transporter for large neutral amino acids activated by the heavy chain of 4F2 antigen (CD98). J Biol Chem 273(37):23629–23632CrossRefPubMedGoogle Scholar
  26. 26.
    Nawashiro H, Otani N, Uozumi Y, Ooigawa H, Toyooka T, Suzuki T, Katoh H, Tsuzuki N, Ohnuki A, Shima K, Shinomiya N, Matsuo H, Kanai Y (2005) High expression of L-type amino acid transporter 1 in infiltrating glioma cells. Brain Tumor Pathol 22(2):89–91. CrossRefPubMedGoogle Scholar
  27. 27.
    Okubo S, Zhen HN, Kawai N, Nishiyama Y, Haba R, Tamiya T (2010) Correlation of L-methyl-11C-methionine (MET) uptake with L-type amino acid transporter 1 in human gliomas. J Neurooncol 99(2):217–225. CrossRefPubMedGoogle Scholar
  28. 28.
    Saito T, Yamasaki F, Kajiwara Y, Abe N, Akiyama Y, Kakuda T, Takeshima Y, Sugiyama K, Okada Y, Kurisu K (2012) Role of perfusion-weighted imaging at 3T in the histopathological differentiation between astrocytic and oligodendroglial tumors. Eur J Radiol 81(8):1863–1869. CrossRefPubMedGoogle Scholar
  29. 29.
    Zitron IM, Kamson DO, Kiousis S, Juhasz C, Mittal S (2013) In vivo metabolism of tryptophan in meningiomas is mediated by indoleamine 2,3-dioxygenase 1. Cancer Biol Ther 14(4):333–339. CrossRefPubMedPubMedCentralGoogle Scholar
  30. 30.
    Shi R, Jiang T, Si L, Li M (2016) Correlations of magnetic resonance, perfusion-weighed imaging parameters and microvessel density in meningioma. J BUON 21(3):709–713PubMedGoogle Scholar
  31. 31.
    Liao W, Liu Y, Wang X, Jiang X, Tang B, Fang J, Chen C, Hu Z (2009) Differentiation of primary central nervous system lymphoma and high-grade glioma with dynamic susceptibility contrast-enhanced perfusion magnetic resonance imaging. Acta Radiol 50(2):217–225. CrossRefPubMedGoogle Scholar
  32. 32.
    Verger A, Stoffels G, Bauer EK, Lohmann P, Blau T, Fink GR, Neumaier B, Shah NJ, Langen KJ, Galldiks N (2017) Static and dynamic 18F-FET PET for the characterization of gliomas defined by IDH and 1p/19q status. Eur J Nucl Med Mol Imaging. Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Yuichi Nomura
    • 1
    • 3
  • Yoshitaka Asano
    • 1
    • 2
  • Jun Shinoda
    • 1
    • 2
  • Hirohito Yano
    • 3
  • Yuka Ikegame
    • 1
    • 2
  • Tomohiro Kawasaki
    • 1
    • 3
  • Noriyuki Nakayama
    • 3
  • Takashi Maruyama
    • 4
  • Yoshihiro Muragaki
    • 4
  • Toru Iwama
    • 3
  1. 1.Chubu Medical Center for Prolonged Traumatic Brain DysfunctionKizawa Memorial HospitalMinokamoJapan
  2. 2.Department of Clinical Brain SciencesGifu University Graduate School of MedicineMinokamoJapan
  3. 3.Department of NeurosurgeryGifu University Graduate School of MedicineGifuJapan
  4. 4.Department of NeurosurgeryTokyo Women’s Medical UniversityTokyoJapan

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