Characteristics of time-activity curves obtained from dynamic 11C-methionine PET in common primary brain tumors
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.
KeywordsMethionine-PET Time activity curve Brain tumor Dynamic PET 3D-acquisition mode
Blood brain barrier
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.
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 was obtained from all individual participants included in the study.
- 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.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. https://doi.org/10.1148/radiology.195.1.7892494 CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.3171/jns.2005.103.3.0498 CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1093/neuonc/now058 CrossRefPubMedPubMedCentralGoogle Scholar
- 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. https://doi.org/10.2967/jnumed.107.048082 CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1097/RLU.0b013e3182291b40 CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1093/neuonc/noq196 CrossRefPubMedPubMedCentralGoogle Scholar
- 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. https://doi.org/10.1002/ijc.29259 CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1093/neuonc/nov088 PubMedPubMedCentralGoogle Scholar
- 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. https://doi.org/10.1093/neuonc/now149 PubMedGoogle Scholar
- 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. https://doi.org/10.1007/s00259-007-0557-4 CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1093/neuonc/nos259 CrossRefPubMedPubMedCentralGoogle Scholar
- 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. https://doi.org/10.1097/RLU.0b013e318262af48 CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1007/s10014-005-0188-z CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1016/j.ejrad.2011.04.009 CrossRefPubMedGoogle Scholar
- 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. https://doi.org/10.1080/02841850802616752 CrossRefPubMedGoogle Scholar