Journal of Neuro-Oncology

, Volume 141, Issue 1, pp 245–252 | Cite as

Brain T1ρ mapping for grading and IDH1 gene mutation detection of gliomas: a preliminary study

  • Mengqiu Cao
  • Weina Ding
  • Xu Han
  • Shiteng Suo
  • Yawen Sun
  • Yao Wang
  • Jianxun Qu
  • Xiaohua Zhang
  • Yan ZhouEmail author
Clinical Study



The longitudinal relaxation time in the rotating frame (T1ρ) has proved to be sensitive to metabolism and useful in application to neurodegenerative diseases. However, few literature exists on its utility in gliomas. Thus, this study was conducted to explore the performance of T1ρ mapping in tumor grading and characterization of isocitrate dehydrogenase 1 (IDH1) gene mutation status of gliomas.


Fifty-seven patients with gliomas underwent brain MRI and quantitative measurements of T1ρ and apparent diffusion coefficient (ADC) were recorded. Parameters were compared between high-grade gliomas (HGG) and low-grade gliomas (LGG) and between IDH1 mutant and wildtype groups.


HGG showed significantly higher T1ρ values in both the solid and peritumoral edema areas compared with LGG (P < 0.001 and P = 0.005, respectively), whereas no significant differences in the two areas were found for ADC (both P > 0.05). Receiver operating characteristic (ROC) curve analysis showed that T1ρ value in the solid area achieved the highest area under the ROC curve (AUC, 0.841) in grading with a sensitivity of 80.6% and a specificity of 81.0%. In the grade II/III glioma group, multivariate logistic regression showed that both tumor frontal lobe location (odds ratio [OR] 526.608; P = 0.045) and T1ρ value of the peritumoral edema area (OR 0.863; P = 0.037) were significant predictors of IDH1 mutation. Using the combination, the diagnostic sensitivity and specificity for IDH1 mutated gliomas were 93.3% and 88.9%, respectively.


Our study shows the feasibility of applying T1ρ mapping in assessing the histologic grade and IDH1 mutation status of gliomas.


Diagnosis Magnetic resonance imaging Glioma Neoplasm grading Isocitrate dehydrogenase 



World Health Organization


Low-grade glioma


High-grade glioma


Isocitrate dehydrogenase 1


Magnetic resonance imaging


Diffusion-weighted imaging


Apparent diffusion coefficient


Longitudinal relaxation time in the rotating frame


Fluid-attenuated inversion recovery


Repetition time


Echo time


Field of view


Spin lock time


Region of interest


Normal-appearing white matter


Intraclass correlation coefficient


Receiver operating characteristic


Area under the curve



This study has received funding by National Natural Science Foundation of China (contract Grant Number: 81701642, 81571650, and 81501458); Shanghai Science and Technology Committee Medical Guide Project (western medicine) (contract Grant Number: 17411964300); and Medical Engineering Cross Research Foundation of Shanghai Jiao Tong University (contract Grant Number: YG2015QN37).

Compliance with ethical standards

Conflict of interest

J. Qu is employee of GE healthcare. The other authors declare that they have no conflict of interest.


  1. 1.
    Louis DN, Perry A, Reifenberger G et al (2016) The 2016 World Health Organization classification of tumors of the central nervous system: a summary. Acta Neuropathol 131:803–820CrossRefGoogle Scholar
  2. 2.
    Park JE, Kim HS, Park KJ, Choi CG, Kim SJ (2015) Histogram analysis of amide proton transfer imaging to identify contrast-enhancing low-grade brain tumor that mimics high-grade tumor: increased accuracy of MR perfusion. Radiology 277:151–161CrossRefGoogle Scholar
  3. 3.
    Stupp R, Mason WP, Mj VDB et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996CrossRefGoogle Scholar
  4. 4.
    Sanai N, Berger MS (2008) Glioma extent of resection and its impact on patient outcome. Neurosurgery 62:264–266CrossRefGoogle Scholar
  5. 5.
    Beiko J, Suki D, Hess KR et al (2014) IDH1 mutant malignant astrocytomas are more amenable to surgical resection and have a survival benefit associated with maximal surgical resection. Neuro-oncology 16:81–91CrossRefGoogle Scholar
  6. 6.
    Yan H, Parsons DW, Jin G et al (2009) IDH1 and IDH2 mutations in gliomas. N Engl J Med 360:765–773CrossRefGoogle Scholar
  7. 7.
    Kono K, Inoue Y, Nakayama K et al (2001) The role of diffusion-weighted imaging in patients with brain tumors. AJNR Am J Neuroradiol 22:1081–1088Google Scholar
  8. 8.
    Kang Y, Choi SH, Kim Y-J et al (2011) Gliomas: histogram analysis of apparent diffusion coefficient maps with standard- or high-b-value diffusion-weighted MR imaging—correlation with tumor grade. Radiology 261:882–890CrossRefGoogle Scholar
  9. 9.
    Fan GG, Deng QL, Wu ZH, Guo QY (2006) Usefulness of diffusion/perfusion-weighted MRI in patients with non-enhancing supratentorial brain gliomas: a valuable tool to predict tumour grading? Br J Radiol 79:652–658CrossRefGoogle Scholar
  10. 10.
    Lam WW, Poon WS, Metreweli C (2002) Diffusion MR imaging in glioma: does it have any role in the pre-operation determination of grading of glioma? Clin Radiol 57:219–225CrossRefGoogle Scholar
  11. 11.
    Fuente MIDL, Young RJ, Rubel J et al (2015) Integration of 2-hydroxyglutarate-proton magnetic resonance spectroscopy into clinical practice for disease monitoring in isocitrate dehydrogenase-mutant glioma. Neuro-oncology 18:283–290CrossRefGoogle Scholar
  12. 12.
    Duvvuri U, Kudchodkar S, Reddy R, Leigh JS (2002) T 1ρ relaxation can assess longitudinal proteoglycan loss from articular cartilage in vitro. Osteoarthr Cartil 10:838–844CrossRefGoogle Scholar
  13. 13.
    Regatte RR, Akella SV, Borthakur A, Reddy R (2003) Proton spin-lock ratio imaging for quantitation of glycosaminoglycans in articular cartilage. J Magn Reson Imaging 17:114–121CrossRefGoogle Scholar
  14. 14.
    Wang YX, Yuan J, Chu ES et al (2011) T_(1ρ) MR imaging is sensitive to evaluate liver fibrosis: an experimental study in a rat biliary duct ligation model. Radiology 259:712–719CrossRefGoogle Scholar
  15. 15.
    Haris M, Mcardle E, Fenty M et al (2009) Early marker for Alzheimer’s disease: hippocampus T1rho (T1ρ) estimation. J Magn Reson Imaging 29:1008–1012CrossRefGoogle Scholar
  16. 16.
    Sierra A, Michaeli S, Niskanen JP et al (2008) Water spin dynamics during apoptotic cell death in glioma gene therapy probed by T1rho and T2rho. Magn Reson Med 59:1311–1319CrossRefGoogle Scholar
  17. 17.
    Villanueva-Meyer J, Barajas R, Mabray M et al (2017) Differentiation of brain tumor-related edema based on 3D T1rho imaging. Eur J Radiol 91:88–92CrossRefGoogle Scholar
  18. 18.
    Barajas RF Jr, Villanueva-Meyer J, Perry A, Berger M, Cha S (2017) Biologically aggressive regions within glioblastoma identified by spin-lock contrast T1 relaxation in the rotating frame (T1rho) MRI. Radiol Case Rep 12:827–832CrossRefGoogle Scholar
  19. 19.
    Johnson CP, Heo HY, Thedens DR, Wemmie JA, Magnotta VA (2014) Rapid acquisition strategy for functional T1ρ mapping of the brain. Magn Reson Imaging 32:1067–1077CrossRefGoogle Scholar
  20. 20.
    Heo HY, Wemmie JA, Johnson CP, Thedens DR, Magnotta VA (2015) Eccentricity mapping of the human visual cortex to evaluate temporal dynamics of functional T1rho mapping. J Cereb Blood Flow Metab 35:1213–1219CrossRefGoogle Scholar
  21. 21.
    Serverabcdea A (2011) Measurements of diagnostic examination performance using quantitative apparent diffusion coefficient and proton MR spectroscopic imaging in the preoperative evaluation of tumor grade in cerebral gliomas. Eur J Radiol 80:462–470CrossRefGoogle Scholar
  22. 22.
    Choi HS, Kim AH, Ahn SS, Shin NY, Kim J, Lee SK (2013) Glioma grading capability: comparisons among parameters from dynamic contrast-enhanced MRI and ADC value on DWI. Korean J Radiol 14:487–492CrossRefGoogle Scholar
  23. 23.
    DeLong ER, DeLong DM, Clarke-Pearson DL (1988) Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44:837–845CrossRefGoogle Scholar
  24. 24.
    Mangia S, Carpenter AF, Tyan AE, Eberly LE, Garwood M, Michaeli S (2014) Magnetization transfer and adiabatic T1rho MRI reveal abnormalities in normal-appearing white matter of subjects with multiple sclerosis. Mult Scler 20:1066–1073CrossRefGoogle Scholar
  25. 25.
    Magnotta VA, Heo HY, Dlouhy BJ et al (2012) Detecting activity-evoked pH changes in human brain. Proc Natl Acad Sci USA 109:8270–8273CrossRefGoogle Scholar
  26. 26.
    Poptani H, Duvvuri U, Miller CG et al (2001) T1 ρ Imaging of murine brain tumors at 4 T. Acad Radiol 8:42–47CrossRefGoogle Scholar
  27. 27.
    Watts R, Andrews T, Hipko S, Gonyea JV, Filippi CG (2014) In vivo whole-brain T1-rho mapping across adulthood: normative values and age dependence. J Magn Reson Imaging 40:376–382CrossRefGoogle Scholar
  28. 28.
    Mou K, Chen M, Mao Q et al (2010) AQP-4 in peritumoral edematous tissue is correlated with the degree of glioma and with expression of VEGF and HIF-alpha. J Neuro-oncol 100:375–383CrossRefGoogle Scholar
  29. 29.
    Howe FA, Barton SJ, Cudlip SA et al (2003) Metabolic profiles of human brain tumors using quantitative in vivo 1H magnetic resonance spectroscopy. Magn Reson Med 49:223–232CrossRefGoogle Scholar
  30. 30.
    Bai Y, Lin Y, Tian J et al (2016) Grading of gliomas by using monoexponential, biexponential, and stretched exponential diffusion-weighted MR imaging and diffusion kurtosis MR imaging. Radiology 278:496–504CrossRefGoogle Scholar
  31. 31.
    Suh CH, Kim HS, Jung SC, Choi CG, Kim SJ (2018) Imaging prediction of isocitrate dehydrogenase (IDH) mutation in patients with glioma: a systemic review and meta-analysis. Eur Radiol. (Epub ahead of print)Google Scholar
  32. 32.
    Waitkus MS, Diplas BH, Yan H (2016) Isocitrate dehydrogenase mutations in gliomas. Neuro-Oncology 18:16–26CrossRefGoogle Scholar
  33. 33.
    Ohka F, Ito M, Ranjit M et al (2014) Quantitative metabolome analysis profiles activation of glutaminolysis in glioma with IDH1 mutation. Tumour Biol 35:5911–5920CrossRefGoogle Scholar
  34. 34.
    Sasaki M, Knobbe CB, Itsumi M et al (2012) D-2-hydroxyglutarate produced by mutant IDH1 perturbs collagen maturation and basement membrane function. Gene Dev 26:2038–2049CrossRefGoogle Scholar
  35. 35.
    Kickingereder P, Sahm F, Radbruch A et al (2015) IDH mutation status is associated with a distinct hypoxia/angiogenesis transcriptome signature which is non-invasively predictable with rCBV imaging in human glioma. Sci Rep 5:16238CrossRefGoogle Scholar
  36. 36.
    Kettunen MI, Grohn OH, Silvennoinen MJ, Penttonen M, Kauppinen RA (2002) Effects of intracellular pH, blood, and tissue oxygen tension on T1rho relaxation in rat brain. Magn Reson Med 48:470–477CrossRefGoogle Scholar

Copyright information

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

Authors and Affiliations

  1. 1.Department of Radiology, Renji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China
  2. 2.GE Healthcare ChinaShanghaiPeople’s Republic of China
  3. 3.Department of Neurosurgery, Renji Hospital, School of MedicineShanghai Jiao Tong UniversityShanghaiPeople’s Republic of China

Personalised recommendations