Prognostic value of volume-based measurements on 11C-methionine PET in glioma patients

  • Kentaro Kobayashi
  • Kenji Hirata
  • Shigeru Yamaguchi
  • Osamu Manabe
  • Shunsuke Terasaka
  • Hiroyuki Kobayashi
  • Tohru Shiga
  • Naoya Hattori
  • Shinya Tanaka
  • Yuji Kuge
  • Nagara Tamaki
Original Article



11C-methionine (MET) PET is an established diagnostic tool for glioma. Studies have suggested that MET uptake intensity in the tumor is a useful index for predicting patient outcome. Because MET uptake is known to reflect tumor expansion more accurately than MRI, we aimed to elucidate the association between volume-based tumor measurements and patient prognosis.


The study population comprised 52 patients with newly diagnosed glioma who underwent PET scanning 20 min after injection of 370 MBq MET. The tumor was contoured using a threshold of 1.3 times the activity of the contralateral normal cortex. Metabolic tumor volume (MTV) was defined as the total volume within the boundary. Total lesion methionine uptake (TLMU) was defined as MTV times the mean standardized uptake value (SUVmean) within the boundary. The tumor-to-normal ratio (TNR), calculated as the maximum standardized uptake value (SUVmax) divided by the contralateral reference value, was also recorded. All patients underwent surgery (biopsy or tumor resection) targeting the tissue with high MET uptake. The Kaplan-Meier method was used to estimate the predictive value of each measurement.


Grade II tumor was diagnosed in 12 patients (3 diffuse astrocytoma, 2 oligodendroglioma, and 7 oligoastrocytoma), grade III in 18 patients (8 anaplastic astrocytoma, 6 anaplastic oligodendroglioma, and 4 anaplastic oligoastrocytoma), and grade IV in 22 patients (all glioblastoma). TNR, MTV and TLMU were 3.1 ± 1.2, 51.6 ± 49.9 ml and 147.7 ± 153.3 ml, respectively. None of the three measurements was able to categorize the glioma patients in terms of survival when all patients were analyzed. However, when only patients with astrocytic tumor (N = 33) were analyzed (i.e., when those with oligodendroglial components were excluded), MTV and TLMU successfully predicted patient outcome with higher values associated with a poorer prognosis (P < 0.05 and P < 0.01, respectively), while the predictive ability of TNR did not reach statistical significance (P = NS).


MTV and TLMU may be useful for predicting outcome in patients with astrocytic tumor.


Glioma 11C-methionine Positron emission tomography Volume-based parameters 



We thank Eriko Suzuki, Keiichi Magota, and Reiko Usui for their technical assistance.

Conflicts of interest

Funds were provided by the Translational Research Network Program of the Ministry of Education, Culture, Sports, Science and Technology (2014). Author K.H. has received a SNMMI Wagner-Torizuka Fellowship (2013/2015), Hokkaido University in conjunction with the HIROKO International Academic Exchange Foundation (2012), and Bayer Best Research Award of the Japan Radiological Society (2014).

Compliance with ethical standards

Research involving human participants and/or animals

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 principles of the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards.

Informed consent

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

Source of funding

Translational Research Network Program of Ministry of Education, Culture, Sports, Science and Technology (2014); SNMMI Wagner-Torizuka Fellowship (2013/2015); Hokkaido University HIROKO’s Fund for Academic Exchange (2012) Bayer Best Research Award of the Japan Radiological Society (2014).

Supplementary material

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Supplementary Figure 1 (DOCX 164 kb)
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Supplementary Table 1 (DOCX 15 kb)
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Supplementary Table 2 (DOCX 15 kb)
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Supplementary Table 3 (DOCX 15 kb)


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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Kentaro Kobayashi
    • 1
  • Kenji Hirata
    • 1
    • 5
  • Shigeru Yamaguchi
    • 2
  • Osamu Manabe
    • 1
  • Shunsuke Terasaka
    • 2
  • Hiroyuki Kobayashi
    • 2
  • Tohru Shiga
    • 1
  • Naoya Hattori
    • 3
  • Shinya Tanaka
    • 4
  • Yuji Kuge
    • 6
  • Nagara Tamaki
    • 1
  1. 1.Department of Nuclear Medicine, Graduate School of MedicineHokkaido UniversitySapporoJapan
  2. 2.Department of Neurosurgery, Graduate School of MedicineHokkaido UniversitySapporoJapan
  3. 3.Department of Molecular Imaging, Graduate School of MedicineHokkaido UniversitySapporoJapan
  4. 4.Department of Cancer Pathology, Graduate School of MedicineHokkaido UniversitySapporoJapan
  5. 5.Department of Molecular and Medical PharmacologyDavid Geffen School of Medicine at UCLALos AngelesUSA
  6. 6.Central Institute of Isotope ScienceHokkaido UniversitySapporoJapan

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