Skip to main content

Advertisement

Log in

Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival

  • Clinical Study
  • Published:
Journal of Neuro-Oncology Aims and scope Submit manuscript

Abstract

MRI grading of grade II and III gliomas may have an important impact on treatment decisions. Occasionally, both conventional MRI (cMRI) and histology fail to clearly establish the tumour grade. Three cMRI features (no necrosis; no relevant oedema; absent or faint contrast enhancement) previously validated in 196 patients with supratentorial gliomas directed our selection of 68 suspected low-grade gliomas (LGG) that were also investigated by advanced MRI (aMRI), including perfusion weighted imaging (PWI), diffusion weighted imaging (DWI) and spectroscopy. All the gliomas had histopathological diagnoses. Sensitivity and specificity of cMRI pre-operative diagnosis were 78.5 and 38.5 %, respectively, and 85.7 and 53.8 % when aMRI was included, respectively. ROC analysis showed that cut-off values of 1.29 for maximum rCBV, 1.69 for minimum rADC, 2.1 for rCho/Cr ratio could differentiate between LGG and HGG with a sensitivity of 61.5, 53.8, and 53.8 % and a specificity of 54.7, 43 and 64.3 %, respectively. A significantly longer OS was observed in patients with a maximum rCBV <1.46 and minimum rADC >1.69 (80 vs 55 months, p = 0.01; 80 vs 51 months, p = 0.002, respectively). This result was also confirmed when cases were stratified according to pathology (LGG vs HGG). The ability of aMRI to differentiate between LGG and HGG and to predict survival improved as the number of aMRI techniques considered increased. In a selected population of suspected LGG, classification by cMRI underestimated the actual fraction of HGG. aMRI slightly increased the diagnostic accuracy compared to histopathology. However, DWI and PWI were prognostic markers independent of histological grade.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  1. Ruiz J, Lesser GJ (2009) Low-grade gliomas. Curr Treat Options Oncol 10:231–242

    Article  PubMed  Google Scholar 

  2. Lang FF, Gilbert MR (2006) Diffusely infiltrative low-grade gliomas in adults. J Clin Oncol 24:1236–1245

    Article  PubMed  CAS  Google Scholar 

  3. Bulakbasi N, Guvenc I, Onguru O, Erdogan E, Tayfun C, Ucoz T (2004) The added value of the apparent diffusion coefficient calculation to magnetic resonance imaging in the differentiation and grading of malignant brain tumors. J Comput Assist Tomogr 28:735–746

    Article  PubMed  Google Scholar 

  4. Law M, Yang S, Wang H, Babb JS, Johnson G, Cha S, Knopp EA, Zagzag D (2003) Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 24:1989–1998

    PubMed  Google Scholar 

  5. Pallud J, Capelle L, Taillandier L et al (2009) Prognostic significance of imaging contrast enhancement for WHO grade II gliomas. Neuro Oncol 11:176–182

    Article  PubMed  PubMed Central  Google Scholar 

  6. Scarabino T, Popolizio T, Trojsi F, Giannatempo G, Pollice S, Maggialetti N, Carriero A, Di Costanzo A, Tedeschi G, Salvolini U (2009) Role of advanced MR imaging modalities in diagnosing cerebral gliomas. Radiol Med 114:448–460

    Article  PubMed  CAS  Google Scholar 

  7. Kim JH, Chang KH, Na DG, Song IC, Kwon BJ, Han MH, Kim K (2006) 3T 1H-MR spectroscopy in grading of cerebral gliomas: comparison of short and intermediate echo time sequences. Am J Neuroradiol 27:1412–1418

    PubMed  Google Scholar 

  8. Weber M-A, Giesel FL, Stieltjes B (2008) MRI for identification of progression in brain tumors: from morphology to function. Expert Rev Neurother 8:1507–1525

    Article  PubMed  Google Scholar 

  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 79:652–658

    Article  PubMed  CAS  Google Scholar 

  10. Chaskis C, Stadnik T, Michotte A, Van Rompaey K, D’Haens J (2006) Prognostic value of perfusion-weighted imaging in brain glioma: a prospective study. Acta Neurochir (Wien) 148:277–285

    Article  CAS  Google Scholar 

  11. Arvinda HR, Kesavadas C, Sarma PS, Thomas B, Radhakrishnan VV, Gupta AK, Kapilamoorthy TR, Nair S (2009) Glioma grading: sensitivity, specificity, positive and negative predictive values of diffusion and perfusion imaging. J Neurooncol 94:87–96

    Article  PubMed  CAS  Google Scholar 

  12. McKnight TR, Lamborn KR, Love TD, Berger MS, Chang S, Dillon WP, Bollen A, Nelson SJ (2007) Correlation of magnetic resonance spectroscopic and growth characteristics within Grades II and III gliomas. J Neurosurg 106:660–666

    Article  PubMed  CAS  Google Scholar 

  13. Chang SM, Nelson S, Vandenberg S et al (2009) Integration of preoperative anatomic and metabolic physiologic imaging of newly diagnosed glioma. J Neurooncol 92:401–415

    Article  PubMed  PubMed Central  Google Scholar 

  14. Sugahara T, Korogi Y, Kochi M et al (1999) Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9:53–60

    Article  PubMed  CAS  Google Scholar 

  15. Gerstner ER, Sorensen AG, Jain RK, Batchelor TT (2008) Advances in neuroimaging techniques for the evaluation of tumor growth, vascular permeability, and angiogenesis in gliomas. Curr Opin Neurol 21:728–735

    Article  PubMed  Google Scholar 

  16. Weber MA, Zoubaa S, Schlieter M et al (2006) Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology 66:1899–1906

    Article  PubMed  CAS  Google Scholar 

  17. Xu V, Chan H, Lin AP, Sailasuta N, Valencerina S, Tran T, Hovener J, Ross BD (2008) MR spectroscopy in diagnosis and neurological decision-making. Semin Neurol 28:407–422

    Article  PubMed  Google Scholar 

  18. Lam WWM, 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 57:219–225

    Article  PubMed  CAS  Google Scholar 

  19. Al-Okaili RN, Krejza J, Wang S, Woo JH, Melhem ER (2006) Advanced MR imaging techniques in the diagnosis of intraaxial brain tumors in adults. Radiographics 26(Suppl 1):S173–S189

    Article  PubMed  Google Scholar 

  20. Hlaihel C, Guilloton L, Guyotat J, Streichenberger N, Honnorat J, Cotton F (2010) Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas. J Neurooncol 97:73–80

    Article  PubMed  Google Scholar 

  21. Morita N, Wang S, Chawla S, Poptani H, Melhem ER (2010) Dynamic susceptibility contrast perfusion weighted imaging in grading of nonenhancing astrocytomas. J Magn Reson Imaging 32:803–808

    Article  PubMed  Google Scholar 

  22. Server A, Kulle B, Gadmar ØB, Josefsen R, Kumar T, Nakstad PH (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 80:462–470

    Article  PubMed  Google Scholar 

  23. Murakami R, Hirai T, Sugahara T et al (2009) Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one- versus two-parameter pilot method. Radiology 251:838–845

    Article  PubMed  Google Scholar 

  24. Khalid L, Carone M, Dumrongpisutikul N, Intrapiromkul J, Bonekamp D, Barker PB, Yousem DM (2012) Imaging characteristics of oligodendrogliomas that predict grade. Am J Neuroradiol 33:852–857

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  25. Sahin N, Melhem ER, Wang S, Krejza J, Poptani H, Chawla S, Verma G (2013) Advanced MR imaging techniques in the evaluation of nonenhancing gliomas: perfusion-weighted imaging compared with proton magnetic resonance spectroscopy and tumor grade. Neuroradiol J 26:531–541

    Article  PubMed  Google Scholar 

  26. Liu Z, Zhou Q, Zeng Q, Li C-F, Zhang K (2012) Noninvasive evaluation of cerebral glioma grade by using diffusion-weighted imaging-guided single-voxel proton magnetic resonance spectroscopy. J Int Med Res 40:76–84

    Article  PubMed  Google Scholar 

  27. Law M, Young RJ, Babb JS, Peccerelli N, Chheang S, Gruber ML, Miller DC, Golfinos JG (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 2:490–498

    Article  Google Scholar 

  28. Maia ACM, Malheiros SMF, da Rocha AJ, da Silva CJ, Gabbai AA, Ferraz FAP, Stávale JN (2005) MR cerebral blood volume maps correlated with vascular endothelial growth factor expression and tumor grade in nonenhancing gliomas. AJNR Am J Neuroradiol 26:777–783

    PubMed  Google Scholar 

  29. Cui Y, Ma L, Chen X, Zhang Z, Jiang H, Lin S (2014) Lower apparent diffusion coefficients indicate distinct prognosis in low-grade and high-grade glioma. J Neurooncol 119:377–385

    Article  PubMed  Google Scholar 

  30. Zulfiqar M, Yousem DM, Lai H (2013) ADC values and prognosis of malignant astrocytomas: does lower ADC predict a worse prognosis independent of grade of tumor?-a meta-analysis. Am J Roentgenol 200:624–629

    Article  Google Scholar 

  31. Caseiras GB, Chheang S, Babb J et al (2010) Relative cerebral blood volume measurements of low-grade gliomas predict patient outcome in a multi-institution setting. Eur J Radiol 73:215–220

    Article  PubMed  Google Scholar 

  32. Bisdas S, Kirkpatrick M, Giglio P, Welsh C, Spampinato MV, Rumboldt Z (2009) Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease? Am J Neuroradiol 30:681–688

    Article  PubMed  CAS  Google Scholar 

  33. Majchrzak K, Kaspera W, Bobek-Billewicz B, Hebda A, Stasik-Pres G, Majchrzak H, Ladzinski P (2012) The assessment of prognostic factors in surgical treatment of low-grade gliomas: a prospective study. Clin Neurol Neurosurg 114:1135–1144

    Article  PubMed  Google Scholar 

  34. Hattingen E, Delic O, Franz K, Pilatus U, Raab P, Lanfermann H, Gerlach R (2010) (1)H MRSI and progression-free survival in patients with WHO grades II and III gliomas. Neurol Res 32:593–602

    Article  PubMed  Google Scholar 

  35. Lober RM, Cho YJ, Tang Y, Barnes PD, Edwards MS, Vogel H, Fisher PG, Monje M, Yeom KW (2014) Diffusion-weighted MRI derived apparent diffusion coefficient identifies prognostically distinct subgroups of pediatric diffuse intrinsic pontine glioma. J. Neurooncol. 117:175–182

    Article  PubMed  Google Scholar 

  36. Zonari P, Baraldi P, Crisi G (2007) Multimodal MRI in the characterization of glial neoplasms: the combined role of single-voxel MR spectroscopy, diffusion imaging and echo-planar perfusion imaging. Neuroradiology 49:795–803

    Article  PubMed  Google Scholar 

  37. Senft C, Hattingen E, Pilatus U, Franz K, Schänzer A, Lanfermann H, Seifert V, Gasser T (2009) Diagnostic value of proton magnetic resonance spectroscopy in the noninvasive grading of solid gliomas: comparison of maximum and mean choline values. Neurosurgery 65:908–913

    Article  PubMed  Google Scholar 

  38. Macdonald DR, Cascino TL, Schold SC Jr, Cairncross JG (1990) Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 8:1277–1280

    PubMed  CAS  Google Scholar 

  39. Van den Bent MJ, Wefel JS, Schiff D et al (2011) Response assessment in neuro-oncology (a report of the RANO group): assessment of outcome in trials of diffuse low-grade gliomas. Lancet Oncol 12:583–593

    Article  PubMed  Google Scholar 

  40. Louis DN, Ohgaki H, Wiestler OD, Cavenee WK, Burger PC, Jouvet A, Scheithauer BW, Kleihues P (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109

    Article  PubMed  PubMed Central  Google Scholar 

  41. Roy B, Gupta RK, Maudsley AA et al (2013) Utility of multiparametric 3-T MRI for glioma characterization. Neuroradiology 55:603–613

    Article  PubMed  PubMed Central  Google Scholar 

  42. Yoon JH, Kim JH, Kang WJ, Sohn C-HH, Choi SH, Yun TJ, Eun Y, Song YS, Chang K-HH (2014) Grading of cerebral glioma with multiparametric MR imaging and 18F-FDG-PET: concordance and accuracy. Eur Radiol 24:380–389

    Article  PubMed  Google Scholar 

  43. Eoli M, Menghi F, Bruzzone MG et al (2007) Methylation of O6-methylguanine DNA methyltransferase and loss of heterozygosity on 19q and/or 17p are overlapping features of secondary glioblastomas with prolonged survival. Clin Cancer Res 13:2606–2613

    Article  PubMed  CAS  Google Scholar 

  44. Pallud J, Blonski M, Mandonnet E, Audureau E, Fontaine D, Sanai N, Bauchet L, Peruzzi P, Frenay M, Colin P, Guillevin R, Bernier V, Baron MH, Guyotat J, Duffau H, Taillandier L, Capelle L (2013) Velocity of tumor spontaneous expansion predicts long-term outcomes for diffuse low-grade gliomas. Neuro-oncol 15(5):595–606

    Article  PubMed  PubMed Central  Google Scholar 

  45. Cancer Genome Atlas Research Network (2015) Comprehensive, integrative genomic analysis of diffuse lower-grade gliomas. NEJM 372(26):2481–2498

    Article  Google Scholar 

  46. Haegler K, Wiesmann M, Böhm C, Freiherr J, Schnell O, Brückmann H, Tonn JC, Linn J (2012) New similarity search based glioma grading. Neuroradiology 54:829–837

    Article  PubMed  Google Scholar 

  47. Bradac O, Vrana J, Jiru F, Kramar F, Netuka D, Hrabal P, Horinek D, de Lacy P, Benes V (2014) Recognition of anaplastic foci within low-grade gliomas using MR spectroscopy. Br J Neurosurg 28:631–636

    Article  PubMed  Google Scholar 

  48. Hilario A, Ramos A, Perez-Nuñez A, Salvador E, Millan JM, Lagares A, Sepulveda JM, Gonzalez-Leon P, Hernandez-Lain A, Ricoy JR (2012) The added value of apparent diffusion coefficient to cerebral blood volume in the preoperative grading of diffuse gliomas. Am J Neuroradiol 33:701–707

    Article  PubMed  CAS  Google Scholar 

  49. Liu X, Tian W, Kolar B, Yeaney GA, Qiu X, Johnson MD, Ekholm S (2011) MR diffusion tensor and perfusion-weighted imaging in preoperative grading of supratentorial nonenhancing gliomas. Neuro Oncol 13:447–455

    Article  PubMed  PubMed Central  Google Scholar 

  50. Cha S, Tihan T, Crawford F, Fischbein NJ, Chang S, Bollen A, Nelson SJ, Prados M, Berger MS, Dillon WP (2005) Differentiation of low-grade oligodendrogliomas from low-grade astrocytomas by using quantitative blood-volume measurements derived from dynamic susceptibility contrast-enhanced MR imaging. Am J Neuroradiol 26:266–273

    PubMed  Google Scholar 

Download references

Acknowledgments

This work was supported by funding from the Associazione Italiana per la Ricerca sul Cancro (AIRC) to G. Filippini and from the Italian Ministry of Health to G. Finocchiaro. We wish to thank Valter Torri and Greta Brenna for their statistical support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Valeria Cuccarini.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical standards

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

Informed consent

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

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (DOC 98 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cuccarini, V., Erbetta, A., Farinotti, M. et al. Advanced MRI may complement histological diagnosis of lower grade gliomas and help in predicting survival. J Neurooncol 126, 279–288 (2016). https://doi.org/10.1007/s11060-015-1960-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11060-015-1960-5

Keywords

Navigation