Association of overall survival in patients with newly diagnosed glioblastoma with contrast-enhanced perfusion MRI: Comparison of intraindividually matched T1- and T2-based bolus techniques

Journal of Magnetic Resonance Imaging

Volumn 42, Issue 1, 2015, Pages 87-96

  Bonekamp, David ^a   Deike, Katerina ^a   Wiestler, Benedikt ^a,b   Wick, Wolfgang ^a,b   Bendszus, Martin ^a   Radbruch, Alexander ^a,b   Heiland, Sabine ^a  

^a UNIVERSITY HOSPITAL HEIDELBERG   (Germany)

^b GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

Author keywords

cerebral blood volume; dynamic contrast enhanced MRI; dynamic susceptibility contrast MRI; glioblastoma; magnetic resonance imaging; overall survival; perfusion imaging; volume transfer constant

Indexed keywords

GADOTERATE MEGLUMINE; CONTRAST MEDIUM; MEGLUMINE; ORGANOMETALLIC COMPOUND;

ADULT; AGED; ARTICLE; BRAIN BLOOD VOLUME; CANCER PROGNOSIS; CLINICAL ARTICLE; CONTRAST ENHANCEMENT; DYNAMIC CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING; DYNAMIC SUSCEPTIBILITY CONTRAST ENHANCED MAGNETIC RESONANCE IMAGING; FEMALE; GLIOBLASTOMA; HUMAN; MALE; NEUROIMAGING; NUCLEAR MAGNETIC RESONANCE SCANNER; OVERALL SURVIVAL; PERFUSION WEIGHTED IMAGING; PRIORITY JOURNAL; RECEIVER OPERATING CHARACTERISTIC; RETROSPECTIVE STUDY; SINGLE DRUG DOSE; SURVIVAL PREDICTION; BIOLOGICAL MODEL; BRAIN NEOPLASMS; COMPARATIVE STUDY; COMPUTER SIMULATION; EPIDEMIOLOGY; GERMANY; IMAGE ENHANCEMENT; INCIDENCE; MAGNETIC RESONANCE ANGIOGRAPHY; MIDDLE AGED; MORTALITY; PROCEDURES; PROGNOSIS; REPRODUCIBILITY; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; STATISTICS AND NUMERICAL DATA; SURVIVAL ANALYSIS; THREE DIMENSIONAL IMAGING; VERY ELDERLY;

ADULT; AGED; AGED, 80 AND OVER; BRAIN NEOPLASMS; COMPUTER SIMULATION; CONTRAST MEDIA; FEMALE; GERMANY; GLIOBLASTOMA; HUMANS; IMAGE ENHANCEMENT; IMAGING, THREE-DIMENSIONAL; INCIDENCE; MAGNETIC RESONANCE ANGIOGRAPHY; MALE; MEGLUMINE; MIDDLE AGED; MODELS, BIOLOGICAL; ORGANOMETALLIC COMPOUNDS; PROGNOSIS; REPRODUCIBILITY OF RESULTS; RISK ASSESSMENT; SENSITIVITY AND SPECIFICITY; SURVIVAL ANALYSIS;

EID: 84931833768     PISSN: 10531807     EISSN: 15222586     Source Type: Journal    
DOI: 10.1002/jmri.24756     Document Type: Article

References (47)

1. Stupp R, Hegi ME, Mason WP, et al., Effects of radiotherapy with concomitant and adjuvant temozolomide versus radiotherapy alone on survival in glioblastoma in a randomised phase III study: 5-year analysis of the EORTC-NCIC trial. Lancet Oncol 2009; 10: 459-466.
2. Smoll NR, Schaller K, Gautschi OP,. Long-term survival of patients with glioblastoma multiforme (GBM). J Clin Neurosci 2013; 20: 670-675.
3. Adeberg S, Bostel T, Konig L, Welzel T, Debus J, Combs SE,. A comparison of long-term survivors and short-term survivors with glioblastoma, subventricular zone involvement: a predictive factor for survival? Radiat Oncol 2014; 9: 95.
4. Chaichana KL, Pendleton C, Chambless L, et al., Multi-institutional validation of a preoperative scoring system which predicts survival for patients with glioblastoma. J Clin Neurosci 2013; 20: 1422-1426.
5. Laws ER, Parney IF, Huang W, et al., Survival following surgery and prognostic factors for recently diagnosed malignant glioma: data from the Glioma Outcomes Project. J Neurosurg 2003; 99: 467-473.
6. Lamborn KR, Chang SM, Prados MD,. Prognostic factors for survival of patients with glioblastoma: recursive partitioning analysis. Neuro Oncol 2004; 6: 227-235.
7. Pope WB, Qiao XJ, Kim HJ, et al., Apparent diffusion coefficient histogram analysis stratifies progression-free and overall survival in patients with recurrent GBM treated with bevacizumab: a multi-center study. J Neurooncol 2012; 108: 491-498.
8. Crawford FW, Khayal IS, McGue C, et al., Relationship of pre-surgery metabolic and physiological MR imaging parameters to survival for patients with untreated GBM. J Neurooncol 2009; 91: 337-351.
9. Saksena S, Jain R, Narang J, et al., Predicting survival in glioblastomas using diffusion tensor imaging metrics. J Magn Reson Imaging 2010; 32: 788-795.
10. Li Y, Lupo JM, Polley MY, et al., Serial analysis of imaging parameters in patients with newly diagnosed glioblastoma multiforme. Neuro Oncol 2011; 13: 546-557.
11. Jain R, Poisson L, Narang J, et al., Genomic mapping and survival prediction in glioblastoma: molecular subclassification strengthened by hemodynamic imaging biomarkers. Radiology 2013; 267: 212-220.
12. Jain R, Poisson LM, Gutman D, et al., Outcome prediction in patients with glioblastoma by using imaging, clinical, and genomic biomarkers: focus on the nonenhancing component of the tumor. Radiology 2014; 272: 484-493.
13. Hirai T, Murakami R, Nakamura H, et al., Prognostic value of perfusion MR imaging of high-grade astrocytomas: long-term follow-up study. AJNR Am J Neuroradiol 2008; 29: 1505-1510.
14. 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.
15. Law M, Yang S, Babb JS, et al., Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 2004; 25: 746-755.
16. Li SP, Padhani AR,. Tumor response assessments with diffusion and perfusion MRI. J Magn Reson Imaging 2012; 35: 745-763.
17. Jalali S, Chung C, Foltz W, et al., MRI biomarkers identify the differential response of glioblastoma multiforme to anti-angiogenic therapy. Neuro Oncol 2014; 16: 868-879.
18. 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.
19. Cha S, Yang L, Johnson G, et al., Comparison of microvascular permeability measurements, K(trans), determined with conventional steady-state T1-weighted and first-pass T2-weighted MR imaging methods in gliomas and meningiomas. AJNR Am J Neuroradiol 2006; 27: 409-417.
20. Kim HS, Ju Goh M, Kim N, Choi CG, Kim SJ, Kim JH,. Which combination of MR imaging modalities is best for predicting recurrent glioblastoma? Study of diagnostic accuracy and reproducibility. Radiology 2014. (Epub ahead of print).
21. Jain R, Narang J, Griffith B, et al., Prognostic vascular imaging biomarkers in high-grade gliomas: tumor permeability as an adjunct to blood volume estimates. Acad Radiol 2013; 20: 478-485.
22. Jenkinson M, Bannister P, Brady M, Smith S,. Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage 2002; 17: 825-841.
23. Smith SM,. Fast robust automated brain extraction. Hum Brain Mapp 2002; 17: 143-155.
24. Knutsson L, van Westen D, Petersen ET, et al., Absolute quantification of cerebral blood flow: correlation between dynamic susceptibility contrast MRI and model-free arterial spin labeling. Magn Reson Imaging 2010; 28: 1-7.
25. Zierler KL,. Equations for measuring blood flow by external monitoring of radioisotopes. Circ Res 1965; 16: 309-321.
26. Kiselev VG,. On the theoretical basis of perfusion measurements by dynamic susceptibility contrast MRI. Magn Reson Med 2001; 46: 1113-1122.
27. Gall P, Emerich P, Kjolby BF, Kellner E, Mader I, Kiselev VG,. On the design of filters for Fourier and oSVD-based deconvolution in bolus tracking perfusion MRI. MAGMA 2010; 23: 187-195.
28. Gall P, Mader I, Kiselev VG,. Extraction of the first bolus passage in dynamic susceptibility contrast perfusion measurements. MAGMA 2009; 22: 241-249.
29. Kind T, Houtzager I, Faes TJ, Hofman MB,. Evaluation of model-independent deconvolution techniques to estimate blood perfusion. Conf Proc IEEE Eng Med Biol Soc 2010; 2010: 2602-2607.
30. Ostergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C, Rosen BR,. High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II. Experimental comparison and preliminary results. Magn Reson Med 1996; 36: 726-736.
31. Grgac K, van Zijl PC, Qin Q,. Hematocrit and oxygenation dependence of blood (1) H(2) O T(1) at 7 tesla. Magn Reson Med 2013; 70: 1153-1159.
32. Biagi L, Abbruzzese A, Bianchi MC, Alsop DC, Del Guerra A, Tosetti M,. Age dependence of cerebral perfusion assessed by magnetic resonance continuous arterial spin labeling. J Magn Reson Imaging 2007; 25: 696-702.
33. Boxerman JL, Schmainda KM, Weisskoff RM,. Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. AJNR Am J Neuroradiol 2006; 27: 859-867.
34. Liu HL, Wu YY, Yang WS, Chen CF, Lim KE, Hsu YY,. Is Weisskoff model valid for the correction of contrast agent extravasation with combined T1 and T2∗ effects in dynamic susceptibility contrast MRI? Med Phys 2011; 38: 802-809.
35. Brunecker P, Villringer A, Schultze J, et al., Correcting saturation effects of the arterial input function in dynamic susceptibility contrast-enhanced MRI: a Monte Carlo simulation. Magn Reson Imaging 2007; 25: 1300-1311.
36. Weinmann HJ, Laniado M, Mutzel W,. Pharmacokinetics of GdDTPA/dimeglumine after intravenous injection into healthy volunteers. Physiol Chem Phys Med NMR 1984; 16: 167-172.
37. 2 relaxation and magnetization transfer in tissue at 3T, Magn Reson Med 2005; 54: 507-512.
38. Pintaske J, Martirosian P, Graf H, et al., Relaxivity of Gadopentetate Dimeglumine (Magnevist), Gadobutrol (Gadovist), and Gadobenate Dimeglumine (MultiHance) in human blood plasma at 0.2, 1.5, and 3 Tesla. Invest Radiol 2006; 41: 213-221.
39. Wetzel SG, Cha S, Johnson G, et al., Relative cerebral blood volume measurements in intracranial mass lesions: interobserver and intraobserver reproducibility study. Radiology 2002; 224: 797-803.
40. Youden WJ,. Index for rating diagnostic tests. Cancer 1950; 3: 32-35.
41. Harrington DP, Fleming TR,. A class of rank test procedures for censored survival data. Biometrika 1982; 69: 553-566.
42. Toh CH, Wei KC, Chang CN, Ng SH, Wong HF,. Differentiation of primary central nervous system lymphomas and glioblastomas: comparisons of diagnostic performance of dynamic susceptibility contrast-enhanced perfusion MR imaging without and with contrast-leakage correction. AJNR Am J Neuroradiol 2013; 34: 1145-1149.
43. Blasel S, Jurcoane A, Bahr O, Weise L, Harter PN, Hattingen E,. MR perfusion in and around the contrast-enhancement of primary CNS lymphomas. J Neurooncology 2013; 114: 127-134.
44. Armitage PA, Schwindack C, Bastin ME, Whittle IR,. Quantitative assessment of intracranial tumor response to dexamethasone using diffusion, perfusion and permeability magnetic resonance imaging. Magn Reson Imaging 2007; 25: 303-310.
45. Knutsson L, Lindgren E, Ahlgren A, et al., Dynamic susceptibility contrast MRI with a prebolus contrast agent administration design for improved absolute quantification of perfusion. Magn Reson Med 2013. (Epub ahead of print).
46. Schmiedeskamp H, Andre JB, Straka M, et al., Simultaneous perfusion and permeability measurements using combined spin- and gradient-echo MRI. J Cereb Blood Flow Metab 2013; 33: 732-743.
47. Ahn SS, Shin NY, Chang JH, et al., Prediction of methylguanine methyltransferase promoter methylation in glioblastoma using dynamic contrast-enhanced magnetic resonance and diffusion tensor imaging. J Neurosurg 2014; 121: 367-373.