MR-Bildgebung bei Gliomen

Philipp Kickingereder; Alexander Radbruch

doi:10.1055/s-0043-101200

TumorDiagnostik & Therapie, Inhaltsverzeichnis

TumorDiagnostik & Therapie 2017; 38(03): 178-184
DOI: 10.1055/s-0043-101200

Thieme Onkologie aktuell

MR-Bildgebung bei Gliomen

Philipp Kickingereder

,

Alexander Radbruch

Abstract

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.

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Referenzen

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; 18: 1673-1679
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 2017; 37: 485-494
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.
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; [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; [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; 22: 5765-5771
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; 18: 1680-1687
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