Neuroradiologie Scan 2016; 06(04): 323-333
DOI: 10.1055/s-0042-118314
Fortbildung
© Georg Thieme Verlag KG Stuttgart · New York

MR-Bildgebung bei Gliomen

Philipp Kickingereder
,
Alexander Radbruch
Further Information

Publication History

Publication Date:
13 October 2016 (online)

Zusammenfassung

Im vorliegenden Übersichtsartikel werden die wesentlichen Entwicklungen der letzten Jahre der MR-Bildgebung bei Gliomen aufgezeigt. Schwerpunkte sind dabei die sog. RANO-Kriterien (Kriterien für das Radiology Assessment in der Neuroonkologie), die umfassende Änderungen in der Bewertung des Therapieansprechens höhergradiger Gliome mit sich brachten, sowie neue, sog. funktionelle MR-Sequenzen. Beschränkte sich die traditionelle Diagnostik bei höhergradigen Gliomen auf kontrastmittelverstärkte T1w Aufnahmen, so wurden mit Einführung der RANO-Kriterien erstmals auch T2w Sequenzen in die Beurteilung des Therapieansprechens einbezogen. Weiterhin wurde in den letzten Jahren der potenzielle Nutzen funktioneller MR-Sequenzen erforscht, die zum Teil Parameter der Tumorbiologie (z. B. Tumorvaskularisation) unmittelbar darstellen können. Nach einer kurzen Vorstellung der wesentlichen, mit Einführung der RANO-Kriterien einhergehenden Änderungen werden in diesem Übersichtsartikel die in der Praxis geläufigsten funktionellen MR-Sequenzen beschrieben: MR-Diffusion, MR-Perfusion und SWI (suszeptibilitätsgewichtete Bildgebung). Darüber hinaus wird ihr potenzieller klinischer Nutzen diskutiert. Abschließend wird ein Ausblick gegeben auf mögliche zukünftige Entwicklungen der MR-Bildgebung der Gliome. Dabei stehen die Ultrahochfeld-MRT bei 7 T (Tesla) sowie die sog. Radiomics im Zentrum der Ausführungen.

 
  • Literatur

  • 1 Wen PY, Macdonald DR, Reardon DA et al. Updated response assessment criteria for high-grade gliomas: response assessment in neuro-oncology working group. J Clin Oncol 2010; 28: 1963-1972
  • 2 Nowosielski M, Wiestler B, Goebel G et al. Progression types after antiangiogenic therapy are related to outcome in recurrent glioblastoma. Neurology 2014; 82: 1684-1692
  • 3 Macdonald DR, Cascino TL, Schold Jr SC et al. Response criteria for phase II studies of supratentorial malignant glioma. J Clin Oncol 1990; 8: 1277-1280
  • 4 Radbruch A, Lutz K, Wiestler B et al. Relevance of T2 signal changes in the assessment of progression of glioblastoma according to the Response Assessment in Neurooncology criteria. Neuro Oncol 2012; 14: 222-229
  • 5 Radbruch A, Fladt J, Kickingereder P et al. Pseudoprogression in patients with glioblastoma: clinical relevance despite low incidence. Neuro Oncol 2015; 17: 151-159
  • 6 Lutz K, Wiestler B, Graf M et al. Infiltrative patterns of glioblastoma: identification of tumor progress using apparent diffusion coefficient histograms. J Magn Reson Imaging 2014; 39: 1096-1103
  • 7 Deike K, Wiestler B, Graf M et al. Prognostic value of combined visualization of MR diffusion and perfusion maps in glioblastoma. J Neurooncol 2016; 126: 463-472
  • 8 Law M, Young RJ, Babb JS et al. Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 2008; 247: 490-498
  • 9 Lev MH, Ozsunar Y, Henson JW et al. Glial tumor grading and outcome prediction using dynamic spin-echo MR susceptibility mapping compared with conventional contrast-enhanced MR: confounding effect of elevated rCBV of oligodendrogliomas [corrected]. AJNR Am J Neuroradiol 2004; 25: 214-221
  • 10 Saito T, Yamasaki F, Kajiwara Y et al. Role of perfusion-weighted imaging at 3T in the histopathological differentiation between astrocytic and oligodendroglial tumors. Eur J Radiol 2012; 81: 1863-1869
  • 11 Burth S, Kickingereder P, Eidel O et al. Clinical parameters outweigh diffusion- and perfusion-derived MRI parameters in predicting survival in newly diagnosed glioblastoma. Neuro Oncol 2016; [Epub ahead of print]
  • 12 Kickingereder P, Radbruch A, Burth S et al. MR-perfusion derived hemodynamic parametric response mapping of bevacizumab efficacy in recurrent glioblastoma. Radiology 2016; 279: 542-552
  • 13 Bonekamp D, Mouridsen K, Radbruch A et al. Assessment of tumor oxygenation and its impact on treatment response in bevacizumab-treated recurrent glioblastoma. J Cereb Blood Flow Metab 2016; [Epub ahead of print]
  • 14 Kickingereder P, Wiestler B, Burth S et al. Relative cerebral blood volume is a potential predictive imaging biomarker of bevacizumab efficacy in recurrent glioblastoma. Neuro Oncol 2015; 17: 1139-1147
  • 15 Vidiri A, Pace A, Fabi A et al. Early perfusion changes in patients with recurrent high-grade brain tumor treated with Bevacizumab: preliminary results by a quantitative evaluation. J Exp Clin Cancer Res 2012; 31: 33
  • 16 Chinot OL, Wick W, Mason W et al. Bevacizumab plus radiotherapy-temozolomide for newly diagnosed glioblastoma. N Engl J Med 2014; 370: 709-722
  • 17 Sandmann T, Bourgon R, Garcia J et al. Patients with proneural glioblastoma may derive overall survival benefit from the addition of bevacizumab to first-line radiotherapy and temozolomide: retrospective analysis of the AVAglio trial. J Clin Oncol 2015; 33: 2735-2744
  • 18 Wick W, Brandes AA, Gorlia T et al. EORTC 26101 phase III trial exploring the combination of bevacizumab and lomustine in patients with first progression of a glioblastoma. J Clin Oncol 2016 Abstr. 2001
  • 19 Lu-Emerson C, Duda DG, Emblem KE et al. Lessons from anti-vascular endothelial growth factor and anti-vascular endothelial growth factor receptor trials in patients with glioblastoma. J Clin Oncol 2015; 33: 1197-1213
  • 20 Mayer TM. Can we predict bevacizumab responders in patients with glioblastoma?. J Clin Oncol 2015; 33: 2721-2722
  • 21 Tsien C, Galbán CJ, Chenevert TL et al. Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma. J Clin Oncol 2010; 28: 2293-2299
  • 22 Radbruch A, Bendszus M, Wick W et al. Comment to: Parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma: pitfalls in perfusion MRI in brain tumors: Tsien C, Galbán CJ, Chenevert TL, Johnson TD, Hamstra DA, Sundgren PC, Junck L, Meyer CR, Rehemtulla A, Lawrence T, Ross BD. J Clin Oncol 2010; 28: 2293-2299 Clin Neuroradiol 2010; 20 (3): 183–184
  • 23 Kickingereder P, Sahm F, Wiestler B et al. Evaluation of microvascular permeability with dynamic contrast-enhanced MRI for the differentiation of primary CNS lymphoma and glioblastoma: radiologic-pathologic correlation. AJNR Am J Neuroradiol 2014; 35: 1503-1508
  • 24 Kickingereder P, Wiestler B, Sahm F et al. Primary central nervous system lymphoma and atypical glioblastoma: multiparametric differentiation by using diffusion-, perfusion-, and susceptibility-weighted MR imaging. Radiology 2014; 272: 843-850
  • 25 Pope WB, Kim HJ, Huo J et al. Recurrent glioblastoma multiforme: ADC histogram analysis predicts response to bevacizumab treatment. Radiology 2009; 252: 182-189
  • 26 Rieger J, Bähr O, Müller K et al. Bevacizumab-induced diffusion-restricted lesions in malignant glioma patients. J Neurooncol 2010; 99: 49-56
  • 27 Mong S, Ellingson BM, Nghiemphu PL et al. Persistent diffusion-restricted lesions in bevacizumab-treated malignant gliomas are associated with improved survival compared with matched controls. AJNR Am J Neuroradiol 2012; 33: 1763-1770
  • 28 Reichenbach JR, Haacke EM. High-resolution BOLD venographic imaging: a window into brain function. NMR Biomed 2001; 14: 453-467
  • 29 Reichenbach JR, Venkatesan R, Schillinger DJ et al. Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. Radiology 1997; 204: 272-277
  • 30 Radbruch A, Graf M, Kramp L et al. Differentiation of brain metastases by percentagewise quantification of intratumoral-susceptibility-signals at 3Tesla. Eur J Radiol 2012; 81: 4064-4068
  • 31 Deistung A, Schweser F, Wiestler B et al. Quantitative susceptibility mapping differentiates between blood depositions and calcifications in patients with glioblastoma. PLoS One 2013; 8: e57924
  • 32 Lupo JM, Essock-Burns E, Molinaro AM et al. Using susceptibility-weighted imaging to determine response to combined anti-angiogenic, cytotoxic, and radiation therapy in patients with glioblastoma multiforme. Neuro Oncol 2013; 15: 480-489
  • 33 Schweser F, Deistung A, Lehr BW et al. Differentiation between diamagnetic and paramagnetic cerebral lesions based on magnetic susceptibility mapping. Med Phys 2010; 37: 5165-5178
  • 34 Bähr O, Harter PN, Weise LM et al. Sustained focal antitumor activity of bevacizumab in recurrent glioblastoma. Neurology 2014; 83: 227-234
  • 35 Radbruch A, Schlemmer HP. Application of ultrahigh-field MRI in neuro-oncology. Radiologe 2013; 53: 411-414
  • 36 Balchandani P, Naidich TP. Ultra-high-field MR neuroimaging. AJNR Am J Neuroradiol 2015; 36: 1204-1215
  • 37 Radbruch A, Eidel O, Wiestler B et al. Quantification of tumor vessels in glioblastoma patients using time-of-flight angiography at 7 Tesla: a feasibility study. Plos One 2014; 9: e110727
  • 38 Nagel AM, Bock M, Hartmann C et al. The potential of relaxation-weighted sodium magnetic resonance imaging as demonstrated on brain tumors. Invest Radiol 2011; 46: 539-547
  • 39 Nagel AM, Lehmann-Horn F, Weber MA et al. In vivo 35Cl MR imaging in humans: a feasibility study. Radiology 2014; 271: 585-595
  • 40 Hoffmann SH, Radbruch A, Bock M et al. Direct (17)O MRI with partial volume correction: first experiences in a glioblastoma patient. MAGMA 2014; 27: 579-587
  • 41 Paech D, Zaiss M, Meissner JE et al. Nuclear overhauser enhancement mediated chemical exchange saturation transfer imaging at 7 Tesla in glioblastoma patients. PLoS One 2014; 9: e104181
  • 42 Paech D, Burth S, Windschuh J et al. Nuclear Overhauser enhancement imaging of glioblastoma at 7 Tesla: region specific correlation with apparent diffusion coefficient and histology. PLoS One 2015; 10(3) e0121220. DOI: 10.1371/journal.pone.0121220.
  • 43 Zaiss M, Kunz P, Goerke S et al. MR imaging of protein folding in vitro employing nuclear-Overhauser-mediated saturation transfer. NMR Biomed 2013; 26: 1815-1822
  • 44 Zaiss M, Windschuh J, Goerke S et al. Downfield-NOE-suppressed amide-CEST-MRI at 7 Tesla provides a unique contrast in human glioblastoma. Magn Reson Med 2016; DOI: 10.1002/mrm.26100. [Epub ahead of print]
  • 45 Schuenke P, Koehler C, Korzowski A et al. Adiabatically prepared spin-lock approach for T1rho-based dynamic glucose enhanced MRI at ultrahigh fields. Magn Reson Med 2016; DOI: 10.1002/mrm.26370. [Epub ahead of print]
  • 46 Lambin P, Rios-Velazquez E, Leijenaar R et al. Radiomics: extracting more information from medical images using advanced feature analysis. Eur J Cancer 2012; 48: 441-446
  • 47 Kickingereder P et al. Large-scale radiomic profiling of recurrent glioblastoma identifies an imaging predictor for stratifying anti-angiogenic treatment response. Clin Cancer Res 2016; [in press]
  • 48 Kickingereder P, Burth S, Wick A et al. Radiomic profiling of glioblastoma: identifying an imaging predictor of patient survival with improved performance over established clinical and radiologic risk models. Radiology 2016; 280: 880-889
  • 49 Chang K, Zhang B, Guo X et al. Multimodal imaging patterns predict survival in recurrent glioblastoma patients treated with bevacizumab. Neuro Oncol 2016; [Epub ahead of print]
  • 50 Aerts HJ, Velazquez ER, Leijenaar RT et al. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach. Nat Commun 2014; 5: 4006
  • 51 Fehr D, Veeraraghavan H, Wibmer A et al. Automatic classification of prostate cancer Gleason scores from multiparametric magnetic resonance images. Proc Natl Acad Sci U S A 2015; 112: E6265-E6273
  • 52 Parmar C, Grossmann P, Bussink J et al. Machine learning methods for quantitative radiomic biomarkers. Sci Rep 2015; 5: 13087