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Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas

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Abstract

The aim of our study was to evaluate the role of proton magnetic resonance (MR) spectroscopy and MR perfusion in the follow-up of low-grade gliomas, since conventional MR imaging (MRI) is not reliable in detecting the passage from a low- to high-grade tumor. Twenty-one patients with a World Health Organisation (WHO) grade II glioma were followed up using proton MR spectroscopy, perfusion, and conventional MRIs. Follow-up MRIs had been performed at the third month of evolution and then twice a year, with an average of five MR studies per patient. Five out of the 21 patients had an anaplastic transformation. A choline to creatine ratio (choline/creatine ratio) above 2.4 is associated with an 83% risk of a malignant transformation in an average delay of 15.4 months. The choline/creatine ratio at this threshold was more efficient than perfusion MR in detecting the anaplastic transformation, with sensitivity of 80% and specificity of 94%. An increased choline/creatine ratio seemed to occur an average 15 months before the elevation of relative cerebral blood volume (rCBV). The mean annual growth of low-grade glioma was 3.65 mm. A growth rate higher than 3 mm per year was also correlated with greater risk of anaplastic transformation. Proton magnetic resonance spectroscopy should be recommended in the follow-up of low-grade gliomas since the choline/creatine ratio can predict anaplastic transformation before perfusion abnormalities, with high positive predictive value of 83%.

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References

  1. Wrensch M, Rice T, Miike R, McMillan A, Lamborn KR, Aldape K, Prados MD (2006) Diagnostic, treatment, and demographic factors influencing survival in a population-based study of adult glioma patients in the San Francisco Bay Area. Neuro Oncol 8:12–26

    Article  PubMed  Google Scholar 

  2. Hess KR, Broglio KR, Bondy ML (2004) Adult glioma incidence trends in the United States, 1977–2000. Cancer 101:2293–2299

    Article  PubMed  Google Scholar 

  3. Chaichana KL, McGirt MJ, Niranjan A, Olivi A, Burger PC, Quinones-Hinojosa A (2009) Prognostic significance of contrast-enhancing low-grade gliomas in adults and a review of the literature. Neurol Res

  4. Scott JN, Brasher PM, Sevick RJ, Rewcastle NB, Forsyth PA (2002) How often are nonenhancing supratentorial gliomas malignant? A population study. Neurology 59:947–949

    CAS  PubMed  Google Scholar 

  5. White ML, Zhang Y, Kirby P, Ryken TC (2005) Can tumor contrast enhancement be used as a criterion for differentiating tumor grades of oligodendrogliomas? AJNR Am J Neuroradiol 26:784–790

    PubMed  Google Scholar 

  6. Sugahara T, Korogi Y, Kochi M, Ikushima I, Hirai T, Okuda T, Shigematsu Y, Liang L, Ge Y, Ushio Y, Takahashi M (1998) Correlation of MR imaging-determined cerebral blood volume maps with histologic and angiographic determination of vascularity of gliomas. AJR Am J Roentgenol 171:1479–1486

    CAS  PubMed  Google Scholar 

  7. Danchaivijitr N, Waldman AD, Tozer DJ, Benton CE, Brasil Caseiras G, Tofts PS, Rees JH, Jager HR (2008) Low-grade gliomas: do changes in rCBV measurements at longitudinal perfusion-weighted MR imaging predict malignant transformation? Radiology 247:170–178

    Article  PubMed  Google Scholar 

  8. Seo HS, Chang KH, Na DG, Kwon BJ, Lee DH (2008) High b-value diffusion (b = 3000 s/mm2) MR imaging in cerebral gliomas at 3T: visual and quantitative comparisons with b = 1000 s/mm2. AJNR Am J Neuroradiol 29:458–463

    Article  CAS  PubMed  Google Scholar 

  9. Lee EJ, Lee SK, Agid R, Bae JM, Keller A, Terbrugge K (2008) Preoperative grading of presumptive low-grade astrocytomas on MR imaging: diagnostic value of minimum apparent diffusion coefficient. AJNR Am J Neuroradiol 29:1872–1877

    Article  CAS  PubMed  Google Scholar 

  10. Miller BL (1991) A review of chemical issues in 1H NMR spectroscopy: N-acetyl-L-aspartate, creatine and choline. NMR Biomed 4:47–52

    Article  CAS  PubMed  Google Scholar 

  11. Miller BL, Chang L, Booth R, Ernst T, Cornford M, Nikas D, McBride D, Jenden DJ (1996) In vivo 1H MRS choline: correlation with in vitro chemistry/histology. Life Sci 58:1929–1935

    Article  CAS  PubMed  Google Scholar 

  12. Gupta RK, Cloughesy TF, Sinha U, Garakian J, Lazareff J, Rubino G, Rubino L, Becker DP, Vinters HV, Alger JR (2000) Relationships between choline magnetic resonance spectroscopy, apparent diffusion coefficient and quantitative histopathology in human glioma. J Neurooncol 50:215–226

    Article  CAS  PubMed  Google Scholar 

  13. Nafe R, Herminghaus S, Raab P, Wagner S, Pilatus U, Schneider B, Schlote W, Zanella F, Lanfermann H (2003) Preoperative proton-MR spectroscopy of gliomas—correlation with quantitative nuclear morphology in surgical specimen. J Neurooncol 63:233–245

    Article  PubMed  Google Scholar 

  14. 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  CAS  PubMed  Google Scholar 

  15. Urenjak J, Williams SR, Gadian DG, Noble M (1993) Proton nuclear magnetic resonance spectroscopy unambiguously identifies different neural cell types. J Neurosci 13:981–989

    CAS  PubMed  Google Scholar 

  16. Alimenti A, Delavelle J, Lazeyras F, Yilmaz H, Dietrich PY, de Tribolet N, Lovblad KO (2007) Monovoxel 1H magnetic resonance spectroscopy in the progression of gliomas. Eur Neurol 58:198–209

    Article  PubMed  Google Scholar 

  17. Tedeschi G, Lundbom N, Raman R, Bonavita S, Duyn JH, Alger JR, Di Chiro G (1997) Increased choline signal coinciding with malignant degeneration of cerebral gliomas: a serial proton magnetic resonance spectroscopy imaging study. J Neurosurg 87:516–524

    Article  CAS  PubMed  Google Scholar 

  18. Weber MA, Vogt-Schaden M, Bossert O, Giesel FL, Kauczor HU, Essig M (2007) MR perfusion and spectroscopic imaging in WHO grade II astrocytomas. Radiologe 47:812–818

    Article  PubMed  Google Scholar 

  19. Kleihues P, Louis DN, Scheithauer BW, Rorke LB, Reifenberger G, Burger PC, Cavenee WK (2002) The WHO classification of tumors of the nervous system. J Neuropathol Exp Neurol 61:215–225 (discussion 226–219)

    PubMed  Google Scholar 

  20. Herminghaus S, Dierks T, Pilatus U, Moller-Hartmann W, Wittsack J, Marquardt G, Labisch C, Lanfermann H, Schlote W, Zanella FE (2003) Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data. J Neurosurg 98:74–81

    Article  PubMed  Google Scholar 

  21. Spampinato MV, Smith JK, Kwock L, Ewend M, Grimme JD, Camacho DL, Castillo M (2007) Cerebral blood volume measurements and proton MR spectroscopy in grading of oligodendroglial tumors. AJR Am J Roentgenol 188:204–212

    Article  PubMed  Google Scholar 

  22. 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 

  23. Reijneveld JC, van der Grond J, Ramos LM, Bromberg JE, Taphoorn MJ (2005) Proton MRS imaging in the follow-up of patients with suspected low-grade gliomas. Neuroradiology 47:887–891

    Article  CAS  PubMed  Google Scholar 

  24. Dowling C, Bollen AW, Noworolski SM, McDermott MW, Barbaro NM, Day MR, Henry RG, Chang SM, Dillon WP, Nelson SJ, Vigneron DB (2001) Preoperative proton MR spectroscopic imaging of brain tumors: correlation with histopathologic analysis of resection specimens. AJNR Am J Neuroradiol 22:604–612

    CAS  PubMed  Google Scholar 

  25. Croteau D, Scarpace L, Hearshen D, Gutierrez J, Fisher JL, Rock JP, Mikkelsen T (2001) Correlation between magnetic resonance spectroscopy imaging and image-guided biopsies: semiquantitative and qualitative histopathological analyses of patients with untreated glioma. Neurosurgery 49:823–829

    Article  CAS  PubMed  Google Scholar 

  26. Pallud J, Mandonnet E, Duffau H, Kujas M, Guillevin R, Galanaud D, Taillandier L, Capelle L (2006) Prognostic value of initial magnetic resonance imaging growth rates for World Health Organization grade II gliomas. Ann Neurol 60:380–383

    Article  PubMed  Google Scholar 

  27. Jensen RL (2009) Brain tumor hypoxia: tumorigenesis, angiogenesis, imaging, pseudoprogression, and as a therapeutic target. J Neurooncol 92:317–335

    Article  CAS  PubMed  Google Scholar 

  28. Mandonnet E, Delattre JY, Tanguy ML, Swanson KR, Carpentier AF, Duffau H, Cornu P, Van Effenterre R, Alvord EC Jr, Capelle L (2003) Continuous growth of mean tumor diameter in a subset of grade II gliomas. Ann Neurol 53:524–528

    Article  PubMed  Google Scholar 

  29. Noguchi T, Yoshiura T, Hiwatashi A, Togao O, Yamashita K, Nagao E, Shono T, Mizoguchi M, Nagata S, Sasaki T, Suzuki SO, Iwaki T, Kobayashi K, Mihara F, Honda H (2008) Perfusion imaging of brain tumors using arterial spin-labeling: correlation with histopathologic vascular density. AJNR Am J Neuroradiol 29:688–693

    Article  CAS  PubMed  Google Scholar 

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Authors and Affiliations

  1. Department of Radiology, Hospital Lyon Sud, Université de Lyon, Université Lyon 1, Hospices civils de Lyon, Pierre-Benite, France

    Chadi Hlaihel & François Cotton

  2. Department of Neurology, Hôpital d’instruction des armées Desgenettes, Lyon, France

    Laurent Guilloton

  3. Department of Neuro-surgery, Hôpital Neurologique Pierre Wertheimer, Université de Lyon, Université Lyon 1, Hospices civils de Lyon, Bron, France

    Jacques Guyotat

  4. Department of Pathology, Université de Lyon, Université Lyon 1, Hospices civils de Lyon, Bron, France

    Nathalie Streichenberger

  5. Department of Neuro-oncolgy, Hôpital Neurologique Pierre Wertheimer, Université de Lyon, Université Lyon 1, Hospices civils de Lyon, Bron, France

    Jerome Honnorat

Authors
  1. Chadi Hlaihel
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  2. Laurent Guilloton
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  3. Jacques Guyotat
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  4. Nathalie Streichenberger
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  6. François Cotton
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Correspondence to Chadi Hlaihel.

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Hlaihel, C., Guilloton, L., Guyotat, J. et al. Predictive value of multimodality MRI using conventional, perfusion, and spectroscopy MR in anaplastic transformation of low-grade oligodendrogliomas. J Neurooncol 97, 73–80 (2010). https://doi.org/10.1007/s11060-009-9991-4

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  • DOI: https://doi.org/10.1007/s11060-009-9991-4

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