PDF herunterladen
J Neurol Surg A Cent Eur Neurosurg 2012; 73(03): 147-152
DOI: 10.1055/s-0032-1313723
Original Article
Thieme Medical Publishers 333 Seventh Avenue, New York, NY 10001, USA.

Cutoff Value of Choline Concentration Reliably Reveals High-Grade Brain Tumors among Other Contrast-Enhancing Brain Lesions[*]

L. Porto
1   Universitätsklinikum Frankfurt, Neuroradiology, Frankfurt, Germany
,
E. Hattingen
1   Universitätsklinikum Frankfurt, Neuroradiology, Frankfurt, Germany
,
A. Stuecher
1   Universitätsklinikum Frankfurt, Neuroradiology, Frankfurt, Germany
,
S. Herminghaus
1   Universitätsklinikum Frankfurt, Neuroradiology, Frankfurt, Germany
,
H. Lanfermann
2   Medizinische Hochschule Hannover, Neuroradiology, Hannover, Germany
,
U. P. Ulrich Pilatus
3   Universitätsklinikum Frankfurt, Brain Image, Frankfurt, Germany
› Institutsangaben
Weitere Informationen

Publikationsverlauf

Publikationsdatum:
03. Mai 2012 (online)

Auch verfügbar auf
    Lizenzen und Reprints

Siehe auch:

Cutoff Value of Choline Concentration Reliably Reveals High-Grade Brain Tumors among Other Contrast-Enhancing Brain LesionsCent Eur Neurosurg 2011; 72(S 01): e43-e43
DOI: 10.1055/s-0031-1291180

Abstract

Background and Aim To evaluate whether there is a cutoff value for a metabolite concentration measured by 1 H MR spectroscopy (MRS), which can be used to differentiate malignant brain tumors (high-grade gliomas, primary CNS lymphomas [PCNSL] and metastases) from other contrast-enhancing lesions like low-grade gliomas and non-neoplastic lesions.

Material and Methods 1 H MRS was performed in 252 consecutive patients with space-occupying brain lesions which were enhanced with application of a contrast agent. Concentrations of N-acetyl-aspartate, total creatine, choline containing metabolites (total choline, tCho), lipids, and lactate were evaluated from the contrast-enhancing part of the lesions and from the normal appearing brain tissue. Linear discriminant analysis was used to find the best predictor for malignant brain tumors. In addition, receiver operating characteristic analysis (ROC) was performed to determine a cutoff value for the best predictor in detecting malignant brain tumors with a specificity of >95%.

Results All brain tumors and 20 out of 47 nonneoplastic lesions were examined histopathologically. The remaining 27 diagnoses were based on MR imaging, clinical findings, and follow-up. The final diagnosis was 134 high-grade gliomas (WHO grade III/IV), 36 metastases, 9 PCNSL, 8 low-grade gliomas (WHO grade I/II), 34 infections, 9 infarctions, 2 hematomas, and 2 vasculitides. 18 patients were excluded due to insufficient spectral quality. The tCho concentration was the best predictor to differentiate malignant brain tumors from enhancing low-grade gliomas or non-neoplastic lesions (F=26.6 [df: 25.833], p<0.0005). The ROC revealed that a cutoff tCho value, based on an increase of ≥40% compared to normal, yielded a specificity of 100% and a sensitivity of 89.4% to correctly diagnose a malignant brain tumor.

Conclusion 1 H MRS reliably differentiates malignant brain tumors from other contrast-enhancing brain lesions. At least a 40% increase of tCho compared to normal brain tissue indicates a malignant tumor (WHO grade III/IV gliomas, PCNSL, metastases) with >90% specificity and sensitivity.

Keywords

choline - 1 H-MR spectroscopy - cerebral mass lesions - brain tumors - malignant

* This article was originally published online in Central European Neurosurgery on December 21, 2011 (DOI:10.1055/s-0031-1291180)


 

  • References

  • 1 Al-Okaili RN, Krejza J, Woo JH , et al. Intraaxial brain masses: MR imaging-based diagnostic strategy – initial experience. Radiology 2007; 243: 539-550
    CrossrefPubMedGoogle Scholar
  • 2 Beer RD, Barbiroli B, Gobbi G , et al. Absolute metabolite quantification by in vivo NMR spectroscopy: III. Multicentre 1H MRS of the human brain addressed by one and the same data-analysis protocol. Magn Reson Imaging 1998; 16: 1107-1111
    CrossrefPubMedGoogle Scholar
  • 3 Bitsch A, Bruhn H, Vougioukas V , et al. Inflammatory CNS demyelination: Histopathologic correlation with in vivo quantitative proton MR spectroscopy. Am J Neuroradiol 1999; 20: 1619-1627
    PubMedGoogle Scholar
  • 4 Blasel S, Pfeilschifter W, Jansen V , et al. Metabolism and regional cerebral blood volume in autoimmune inflammatory demyelinating lesions mimicking malignant gliomas. J Neurol 2010; 258: 113-122
    PubMedGoogle Scholar
  • 5 Bulakbasi N, Kocaoglu M, Ors F , et al. Combination of single-voxel proton MR spectroscopy and apparent diffusion coefficient calculation in the evaluation of common brain tumors. AJNR Am J Neuroradiol 2003; 24: 225-233
    PubMedGoogle Scholar
  • 6 Christiansen P, Henriksen O, Stubgaard M , et al. In vivo quantification of brain metabolites by 1H-MRS using water as an internal standard. Magn Reson Imaging 1993; 11: 107-118
    CrossrefPubMedGoogle Scholar
  • 7 De Stefano N, Matthews PM, Antel JP , et al. Chemical pathology of acute demyelinating lesions and its correlation with disability. Ann Neurol 1995; 38: 901-909
    CrossrefPubMedGoogle Scholar
  • 8 el-Deredy W. Pattern recognition approaches in biomedical and clinical magnetic resonance spectroscopy: a review. NMR Biomed 1997; 10: 99-124
    CrossrefPubMedGoogle Scholar
  • 9 Evanochko WT, Sakai TT, Ng TC , et al. NMR study of in vivo RIF-1 tumors. Analysis of perchloric acid extracts and identification of 1H, 31P and 13C resonances. Biochim Biophys Acta 1984; 805: 104-116
    CrossrefPubMedGoogle Scholar
  • 10 Galanaud D, Chinot O, Nicoli F , et al. Use of proton magnetic resonance spectroscopy of the brain to differentiate gliomatosis cerebri from low-grade glioma. J Neurosurg 2003; 98: 269-276
    CrossrefPubMedGoogle Scholar
  • 11 Gillies RJ, Barry JA, Ross BD. In vitro and in vivo 13 C and 31 P NMR analyses of phosphocholine metabolism in rat glioma cells. Magn Reson Med 1994; 32: 310-318
    CrossrefPubMedGoogle Scholar
  • 12 Hattingen E, Delic O, Franz K , et al. (1)H MRSI and progression-free survival in patients with WHO grades II and III gliomas. Neurol Res 2010; 32: 593-602
    CrossrefPubMedGoogle Scholar
  • 13 Henriksen O. In vivo quantitation of metabolite concentrations in the brain by means of proton MRS. NMR Biomed 1995; 8: 139-148
    CrossrefPubMedGoogle Scholar
  • 14 Hermann EJ, Hattingen E, Krauss JK , et al. Stereotactic biopsy in gliomas guided by 3-tesla 1H-chemical-shift imaging of choline. Stereotact Funct Neurosurg 2008; 86: 300-307
    CrossrefPubMedGoogle Scholar
  • 15 Herminghaus S, Dierks T, Pilatus U , et al. Determination of histopathological tumor grade in neuroepithelial brain tumors by using spectral pattern analysis of in vivo spectroscopic data. J Neurosurg 2003; 98: 74-81
    CrossrefPubMedGoogle Scholar
  • 16 Hourani R, Brant LJ, Rizk T , et al. Can proton MR spectroscopic and perfusion imaging differentiate between neoplastic and nonneoplastic brain lesions in adults?. AJNR 2008; 29: 366-372
    CrossrefPubMedGoogle Scholar
  • 17 Jackson JR, Seed MP, Kircher CH , et al. The codependence of angiogenesis and chronic inflammation. Faseb J 1997; 11: 457-465
    PubMedGoogle Scholar
  • 18 Jansen JF, Backes WH, Nicolay K , et al. 1H MR spectroscopy of the brain: absolute quantification of metabolites. Radiology 2006; 240: 318-332
    Google Scholar
  • 19 Julià-Sapé M, Acosta D, Majós C , et al. Comparison between neuroimaging classifications and histopathological diagnoses using an international multicenter brain tumor magnetic resonance imaging database. J Neurosurg 2006; 105: 6-14
    CrossrefPubMedGoogle Scholar
  • 20 Kaplan O, Cohen JS. Lymphocyte activation and phospholipids pathways. 31P magnetic resonance studies. J Biol Chem 1991; 266: 3688-3694
    PubMedGoogle Scholar
  • 21 Keevil SF, Barbiroli B, Brooks JC , et al. Absolute metabolite quantification by in vivo NMR spectroscopy: II. A multicentre trial of protocols for in vivo localised proton studies of human brain. Magn Reson Imaging 1998; 16: 1093-1106
    CrossrefPubMedGoogle Scholar
  • 22 Kleihues P, Cavenee WK. (Eds.). Pathology and genetics of tumours of the nervous system. 2nd  Edition. Lyon: IARC Press; 2000
  • 23 Law M, Yang S, Wang H , et al. Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR 2003; 24: 1989-1998
    PubMedGoogle Scholar
  • 24 Majós C, Alonso J, Aguilera C , et al. Proton magnetic resonance spectroscopy ((1)H MRS) of human brain tumours: assessment of differences between tumour types and its applicability in brain tumour categorization. Eur Radiol 2003; 13: 582-591
    PubMedGoogle Scholar
  • 25 Michaelis T, Merboldt KD, Bruhn H , et al. Absolute concentrations of metabolites in the adult human brain in vivo: quantification of localized proton MR spectra. Radiology 1993; 187: 219-227
    CrossrefPubMedGoogle Scholar
  • 26 Möller-Hartmann W, Herminghaus S, Krings T , et al. Clinical application of proton magnetic resonance spectroscopy in the diagnosis of intracranial mass lesions. Neuroradiology 2002; 44: 371-381
    CrossrefPubMedGoogle Scholar
  • 27 Muacevic A, Kreth FW. Significance of stereotactic biopsy for the management of WHO grade II supratentorial glioma. Nervenarzt 2003; 74: 350-354
    CrossrefPubMedGoogle Scholar
  • 28 Panigrahy A, Krieger MD, Gonzalez-Gomez I , et al. Quantitative short echo time 1H-MR spectroscopy of untreated pediatric brain tumors: preoperative diagnosis and characterization. AJNR 2006; 27: 560-572
    PubMedGoogle Scholar
  • 29 Podo F, Ferretti A, Knijn A , et al. Detection of phosphotidylcholine-specific phospholipase C in NIH-3T3 fibroblasts and their H-ras transformants: NMR and immunochemical studies. Anticancer Res 1996; 16: 1399-1412
    PubMedGoogle Scholar
  • 30 Podo F. Tumour phospholipids metabolism. NMR Biomed 1999; 12: 413-439
    CrossrefPubMedGoogle Scholar
  • 31 Poptani H, Gupta RK, Roy R , et al. Characterization of intracranial mass lesions with in vivo proton MR spectroscopy. AJNR 1995; 16: 1593-1603
    PubMedGoogle Scholar
  • 32 Senft C, Hattingen E, Pilatus U , et al. Diagnostic value of proton magnetic resonance spectroscopy in the noninvasive grading of solid gliomas: comparison of maximum and mean choline values. Neurosurgery 2009; 65: 908-913
    CrossrefPubMedGoogle Scholar
  • 33 Stadlbauer A, Gruber S, Nimsky C , et al. Preoperative grading of gliomas by using metabolite quantification with high-spatial-resolution proton MR spectroscopic imaging. Radiology 2006; 238: 958-969
    CrossrefPubMedGoogle Scholar
  • 34 Sze DY, Jardetzky O. Characterization of lipid composition in stimulated human lymphocytes by 1H-NMR. Biochim Biophys Acta 1990; 1054: 198-206
    CrossrefPubMedGoogle Scholar
  • 35 Tate AR, Crabb S, Griffiths JR , et al. Lipid metabolite peaks in pattern recognition analysis of tumour in vivo MR spectra. Anticancer Res 1996; 16: 1575-1579
    PubMedGoogle Scholar
  • 36 Tate AR, Griffiths JR, Martínez-Pérez I , et al. Towards a method for automated classification of 1H MRS spectra from brain tumours. NMR Biomed 1998; 11: 177-191
    CrossrefPubMedGoogle Scholar
  • 37 Vanhamme L, van den Boogaart A, Van Huffel S. Improved method for accurate and efficient quantification of MRS data with use of prior knowledge. J Magn Reson 1997; 129: 35-43
    CrossrefPubMedGoogle Scholar
  • 38 White ML, Zhang Y, Kirby P , et al. Can tumor contrast enhancement be used as a criterion for differentiating tumor grades of oligodendro­gliomas?. AJNR 2005; 26: 784-790
    PubMedGoogle Scholar
  • 39 Yerli H, Ağildere AM, Ozen O , et al. Evaluation of cerebral glioma grade by using normal side creatine as an internal reference in multi-voxel 1H-MR spectroscopy. Diagn Interv Radiol 2007; 13: 3-9
    PubMedGoogle Scholar