Journal of Neuro-Oncology

, Volume 120, Issue 2, pp 361–370 | Cite as

Survival analysis in patients with newly diagnosed primary glioblastoma multiforme using pre- and post-treatment peritumoral perfusion imaging parameters

  • Asim K. Bag
  • Phillip C. Cezayirli
  • Jake J. Davenport
  • Santhosh Gaddikeri
  • Hassan M. Fathallah-Shaykh
  • Alan Cantor
  • Xiaosi S. Han
  • Louis B. Nabors
Clinical Study


The objective of this study was to evaluate if peritumoral (PT) perfusion parameters obtained from dynamic susceptibility weighted contrast enhanced perfusion MRI can predict overall survival (OS) and progression free survival (PFS) in patients with newly diagnosed glioblastoma multiforme (GBM). Twenty-eight newly diagnosed GBM patients, who were treated with resection followed by concurrent chemoradiation and adjuvant chemotherapy, were included in this study. Evaluated perfusion parameters were pre- and post-treatment PT relative cerebral blood volume (rCBV) and relative cerebral blood flow (rCBF). Proportional hazard analysis was used to assess the relationship OS, PFS and perfusion parameters. Kaplan–Meier survival estimates and log-rank test were used to characterize and compare the patient groups with high and low perfusion parameter values in terms of OS and PFS. Pretreatment PT rCBV and rCBF were not associated with OS and PFS whereas there was statistically significant association of both posttreatment PT rCBV and rCBF with OS and posttreatment rCBV with PFS (association of PFS and posttreatment rCBF was not statistically significant). Neither the Kaplan–Meier survival estimates nor the log-rank test demonstrated any differences in OS between high and low pretreatment PT rCBV values and rCBF values; however, high and low post-treatment PT rCBV and rCBF values did demonstrate statistically significant difference in OS and PFS. Our study found posttreatment, not pretreatment, PT perfusion parameters can be used to predict OS and PFS in patients with newly diagnosed GBM.


Peritumoral rCBV and rCBF Perfusion MRI GBM Glioblastoma Overall survival Progression free survival 


Conflict of interest

Asim K. Bag is consultant to “Dotarem Advisory Board”, Guerbet, LLC. Other authors declare no conflict of interest.




  1. 1.
    Kleihues P, Burger PC, Aldape KD et al (2007) Glioblastoma. In: Louis DN, Ohgaki H, Wiestler OD, Cavenee WK (eds) WHO classification of tumors of the central nervous system. IARC Press, Lyon, pp 33–49Google Scholar
  2. 2.
    Mangla R, Singh G, Ziegelitz D, Milano MT, Korones DN, Zhong J, Ekholm SE (2010) Changes in relative cerebral blood volume 1 month after radiation-temozolomide therapy can help predict overall survival in patients with glioblastoma. Radiology 256:575–584. doi: 10.1148/radiol.10091440 PubMedCrossRefGoogle Scholar
  3. 3.
    Choi YJ, Kim HS, Jahng GH, Kim SJ, Suh DC (2013) Pseudoprogression in patients with glioblastoma: added value of arterial spin labeling to dynamic susceptibility contrast perfusion MR imaging. Acta Radiol 54:448–454. doi: 10.1177/0284185112474916 PubMedCrossRefGoogle Scholar
  4. 4.
    Stupp R, Mason WP, van Den Bent MJ, Weller M, Fisher B, Taphoorn MJ, Belanger K, Brandes AA, Marosi C, Bogdahn U, Curschmann J, Janzer RC, Ludwin SK, Gorlia T, Allgeier A, Lacombe D, Cairncross JG, Eisenhauer E, Mirimanoff RO, European Organization for Research and Treatment of Cancer Brain Tumor and Radiotherapy Groups; National Cancer Institute of Canada Clinical Trials Group (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. The N Engl J Med 352:987–996. doi: 10.1056/NEJMoa043330 CrossRefGoogle Scholar
  5. 5.
    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. Am J Neuroradiol 24:1989–1998PubMedGoogle Scholar
  6. 6.
    Cha S, Lupo JM, Chen MH, Lamborn KR, McDermott MW, Berger MS, Nelson SJ, Dillon WP (2007) Differentiation of glioblastoma multiforme and single brain metastasis by peak height and percentage of signal intensity recovery derived from dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Am J Neuroradiol 28:1078–1084. doi: 10.3174/ajnr.A0484 PubMedCrossRefGoogle Scholar
  7. 7.
    Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. Am J Neuroradiol 27:475–487PubMedGoogle Scholar
  8. 8.
    Bisdas S, Kirkpatrick M, Giglio P, Welsh C, Spampinato MV, Rumboldt Z (2009) Cerebral blood volume measurements by perfusion-weighted MR imaging in gliomas: ready for prime time in predicting short-term outcome and recurrent disease? Am J Neuroradiol 30:681–688. doi: 10.3174/ajnr.A1465 PubMedCrossRefGoogle Scholar
  9. 9.
    Hu LS, Baxter LC, Smith KA, Feuerstein BG, Karis JP, Eschbacher JM, Coons SW, Nakaji P, Yeh RF, Debbins J, Heiserman JE (2009) Relative cerebral blood volume values to differentiate high-grade glioma recurrence from posttreatment radiation effect: direct correlation between image-guided tissue histopathology and localized dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging measurements. Am J Neuroradiol 30:552–558. doi: 10.3174/ajnr.A1377 PubMedCrossRefGoogle Scholar
  10. 10.
    Lacerda S, Law M (2009) Magnetic resonance perfusion and permeability imaging in brain tumors. Neuroimaging Clin N Am 19:527–557. doi: 10.1016/j.nic.2009.08.007 PubMedCrossRefGoogle Scholar
  11. 11.
    Hirai T, Murakami R, Nakamura H, Kitajima M, Fukuoka H, Sasao A, Akter M, Hayashida Y, Toya R, Oya N, Awai K, Iyama K, Kuratsu JI, Yamashita Y (2008) Prognostic value of perfusion MR imaging of high-grade astrocytomas: long-term follow-up study. Am J Neuroradiol 29:1505–1510. doi: 10.3174/ajnr.A1121 PubMedCrossRefGoogle Scholar
  12. 12.
    Rollin N, Guyotat J, Streichenberger N, Honnorat J, Minh VAT, Cotton F (2006) Clinical relevance of diffusion and perfusion magnetic resonance imaging in assessing intra-axial brain tumors. Neuroradiology 48:150–159. doi: 10.1007/s00234-005-0030-7 PubMedCrossRefGoogle Scholar
  13. 13.
    Schmainda KM, Rand SD, Joseph AM, Lund R, Ward BD, Pathak AP, Ulmer JL, Badruddoja MA, Krouwer HG (2004) Characterization of a first-pass gradient-echo spin-echo method to predict brain tumor grade and angiogenesis. Am J Neuroradiol 25:1524–1532PubMedGoogle Scholar
  14. 14.
    Chaskis C, Stadnik T, Michotte A, Van Rompaey K, D’Haens J (2006) Prognostic value of perfusion-weighted imaging in brain glioma: a prospective study. Acta Neurochir 148:277–285. doi: 10.1007/s00701-005-0718-9 PubMedCrossRefGoogle Scholar
  15. 15.
    Law M, Yang S, Babb JS, Knopp EA, Golfinos JG, Zagzag D, Johnson G (2004) Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. Am J Neuroradiol 25:746–755PubMedGoogle Scholar
  16. 16.
    Law M, Young RJ, Babb JS, Peccerelli N, Chheang S, Gruber ML, Miller DC, Golfinos JG, Zagzag D, Johnson G (2008) Gliomas: predicting time to progression or survival with cerebral blood volume measurements at dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 247:490–498. doi: 10.1148/radiol.2472070898 PubMedCrossRefPubMedCentralGoogle Scholar
  17. 17.
    Shiroishi MS, Booker MT, Agarwal M, Jain N, Naghi I, Lerner A, Law M (2013) Posttreatment evaluation of central nervous system gliomas. Magn Reson Imaging Clin N Am 21:241–268. doi: 10.1016/j.mric.2013.02.004 PubMedCrossRefGoogle Scholar
  18. 18.
    Chavhan GB, Babyn PS, Thomas B, Shroff MM, Haacke EM (2009) Principles, techniques, and applications of T2*-based MR imaging and its special applications. Radiographics 29:1433–1449. doi: 10.1148/rg.295095034 PubMedCrossRefPubMedCentralGoogle Scholar
  19. 19.
    Barajas RF Jr, Chang JS, Segal MR, Parsa AT, McDermott MW, Berger MS, Cha S (2009) Differentiation of recurrent glioblastoma multiforme from radiation necrosis after external beam radiation therapy with dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging. Radiology 253:486–496. doi: 10.1148/radiol.2532090007 PubMedCrossRefPubMedCentralGoogle Scholar
  20. 20.
    Gasparetto EL, Pawlak MA, Patel SH, Huse J, Woo JH, Krejza J, Rosenfeld MR, O’Rourke DM, Lustig R, Melhem ER, Wolf RL (2009) Posttreatment recurrence of malignant brain neoplasm: accuracy of relative cerebral blood volume fraction in discriminating low from high malignant histologic volume fraction. Radiology 250:887–896. doi: 10.1148/radiol.2502071444 PubMedCrossRefGoogle Scholar
  21. 21.
    Kelly PJ, Daumas-Duport C, Kispert DB, Kall BA, Scheithauer BW, Illig JJ (1987) Imaging-based stereotaxic serial biopsies in untreated intracranial glial neoplasms. J Neurosurg 66:865–874. doi: 10.3171/jns.1987.66.6.0865 PubMedCrossRefGoogle Scholar
  22. 22.
    De Belder FE, Oot AR, Van Hecke W, Venstermans C, Menovsky T, Van Marck V, Van Goethem J, Van den Hauwe L, Vandekerckhove M, Parizel PM (2012) Diffusion tensor imaging provides an insight into the microstructure of meningiomas, high-grade gliomas, and peritumoral edema. J Comput Assist Tomogr 36:577–582. doi: 10.1097/RCT.0b013e318261e913 PubMedCrossRefGoogle Scholar
  23. 23.
    Stewart JG, Sawrie SM, Bag A, Han X, Fiveash JB (2010) Management of brain metastases. Curr Treat Options Neurol. 12:334–346. doi: 10.1007/s11940-010-0074-9 PubMedCrossRefGoogle Scholar
  24. 24.
    Law M, Cha S, Knopp EA, Johnson G, Arnett J, Litt AW (2002) High-grade gliomas and solitary metastases: differentiation by using perfusion and proton spectroscopic MR imaging. Radiology 222:715–721. doi: 10.1148/radiol.2223010558 PubMedCrossRefGoogle Scholar
  25. 25.
    Blasel S, Franz K, Ackermann H, Weidauer S, Zanella F, Hattingen E (2011) Stripe-like increase of rCBV beyond the visible border of glioblastomas: site of tumor infiltration growing after neurosurgery. J Neuro-oncology 103:575–584. doi: 10.1007/s11060-010-0421-4 CrossRefGoogle Scholar
  26. 26.
    Server A, Orheim TE, Graff BA, Josefsen R, Kumar T, Nakstad PH (2011) Diagnostic examination performance by using microvascular leakage, cerebral blood volume, and blood flow derived from 3-T dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging in the differentiation of glioblastoma multiforme and brain metastasis. Neuroradiology 53:319–330. doi: 10.1007/s00234-010-0740-3 PubMedCrossRefGoogle Scholar
  27. 27.
    Lehmann P, Saliou G, de Marco G, Monet P, Souraya SE, Bruniau A, Vallee JN, Ducreux D (2012) Cerebral peritumoral oedema study: does a single dynamic MR sequence assessing perfusion and permeability can help to differentiate glioblastoma from metastasis? Eur J Radiol 81:522–527. doi: 10.1016/j.ejrad.2011.01.076 PubMedCrossRefGoogle Scholar
  28. 28.
    Hakyemez B, Erdogan C, Ercan I, Ergin N, Uysal S, Atahan S (2005) High-grade and low-grade gliomas: differentiation by using perfusion MR imaging. Clin Radiol 60:493–502. doi: 10.1016/j.crad.2004.09.009 PubMedCrossRefGoogle Scholar
  29. 29.
    Thomsen H, Steffensen E, Larsson EM (2012) Perfusion MRI (dynamic susceptibility contrast imaging) with different measurement approaches for the evaluation of blood flow and blood volume in human gliomas. Acta Radiol 53:95–101. doi: 10.1258/ar.2011.110242 PubMedCrossRefGoogle Scholar
  30. 30.
    Law M, Young R, Babb J, Rad M, Sasaki T, Zagzag D, Johnson G (2006) Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. Am J Neuroradiol 27:1975–1982PubMedGoogle Scholar
  31. 31.
    Parikh AH, Smith JK, Ewend MG, Bullitt E (2004) Correlation of MR perfusion imaging and vessel tortuosity parameters in assessment of intracranial neoplasms. Technol Cancer Res Treat 3:585–590PubMedCrossRefPubMedCentralGoogle Scholar
  32. 32.
    Kleihues P, Burger PC, Aldape KD et al (2007) Glioblastoma. In: Louis DN, Ohgaki H, Wiestler OD, Cavenee WK (eds) WHO classification of tumors of the central nervous system. IARC Press, Lyon, pp 33–49Google Scholar
  33. 33.
    Broglio KR, Berry DA (2009) Detecting an overall survival benefit that is derived from progression-free survival. J Natl Cancer Inst 101:1642–1649. doi: 10.1093/jnci/djp369 PubMedCrossRefPubMedCentralGoogle Scholar
  34. 34.
    Boxerman JL, Schmainda KM, Weisskoff RM (2006) Relative cerebral blood volume maps corrected for contrast agent extravasation significantly correlate with glioma tumor grade, whereas uncorrected maps do not. Am J Neuroradiol 27:859–867PubMedGoogle Scholar
  35. 35.
    Wetzel SG, Cha S, Johnson G, Lee P, Law M, Kasow DL, Pierce SD, Xue X (2002) Relative cerebral blood volume measurements in intracranial mass lesions: interobserver and intraobserver reproducibility study. Radiology 224:797–803PubMedCrossRefGoogle Scholar
  36. 36.
    Young R, Babb J, Law M, Pollack E, Johnson G (2007) Comparison of region-of-interest analysis with three different histogram analysis methods in the determination of perfusion metrics in patients with brain gliomas. J Magn Reson Imaging 26:1053–1063. doi: 10.1002/jmri.21064 PubMedCrossRefGoogle Scholar
  37. 37.
    Hu LS, Eschbacher JM, Heiserman JE, Dueck AC, Shapiro WR, Liu S, Karis JP, Smith KA, Coons SW, Nakaji P, Spetzler RF, Feuerstein BG, Debbins J, Baxter LC (2012) Reevaluating the imaging definition of tumor progression: perfusion MRI quantifies recurrent glioblastoma tumor fraction, pseudoprogression, and radiation necrosis to predict survival. Neuro-oncology 14:919–930. doi: 10.1093/neuonc/nos112 PubMedCrossRefPubMedCentralGoogle Scholar
  38. 38.
    Cha S, Knopp EA, Johnson G, Wetzel SG, Litt AW, Zagzag D (2002) Intracranial mass lesions: dynamic contrast-enhanced susceptibility-weighted echo-planar perfusion MR imaging. Radiology 223:11–29. doi: 10.1148/radiol.2231010594 PubMedCrossRefGoogle Scholar
  39. 39.
    Lev MH, Ozsunar Y, Henson JW, Rasheed AA, Barest GD, Harsh GR, Fitzek MM, Chiocca EA, Rabinov JD, Csavoy AN, Rosen BR, Hochberg FH, Schaefer PW, Gonzalez RG (2004) 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]. Am J Neuroradiol 25:214–221PubMedGoogle Scholar
  40. 40.
    Law M, Oh S, Babb JS, Wang E, Inglese M, Zagzag D, Knopp EA, Johnson G (2006) Low-grade gliomas: dynamic susceptibility-weighted contrast-enhanced perfusion MR imaging–prediction of patient clinical response. Radiology 238:658–667. doi: 10.1148/radiol.2382042180 PubMedCrossRefGoogle Scholar
  41. 41.
    Mills SJ, Patankar TA, Haroon HA, Baleriaux D, Swindell R, Jackson A (2006) Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? Am J Neuroradiol 27:853–858PubMedGoogle Scholar
  42. 42.
    Haris M, Husain N, Singh A, Husain M, Srivastava S, Srivastava C, Behari S, Rathore RK, Saksena S, Gupta RK (2008) Dynamic contrast-enhanced derived cerebral blood volume correlates better with leak correction than with no correction for vascular endothelial growth factor, microvascular density, and grading of astrocytoma. J Comput Assist Tomogr 32:955–965. doi: 10.1097/RCT.0b013e31816200d1 PubMedCrossRefGoogle Scholar
  43. 43.
    Majchrzak K, Kaspera W, Szymas J, Bobek-Billewicz B, Hebda A, Majchrzak H (2013) Markers of angiogenesis (CD31, CD34, rCBV) and their prognostic value in low-grade gliomas. Neurol Neurochir Pol 47:325–331. doi: 10.5114/ninp.2013.36757 PubMedGoogle Scholar
  44. 44.
    Gilbert MR, Dignam JJ, Armstrong TS, Wefel JS, Blumenthal DT, Vogelbaum MA, Colman H, Chakravarti A, Pugh S, Won M, Jeraj R, Brown PD, Jaeckle KA, Schiff D, Stieber VW, Brachman DG, Werner-Wasik M, Tremont-Lukats IW, Sulman EP, Aldape KD, Curran WJ Jr, Mehta MP (2014) A randomized trial of bevacizumab for newly diagnosed glioblastoma. N Engl J Med 370:699–708. doi: 10.1056/NEJMoa1308573 PubMedCrossRefPubMedCentralGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Asim K. Bag
    • 1
  • Phillip C. Cezayirli
    • 3
  • Jake J. Davenport
    • 1
  • Santhosh Gaddikeri
    • 2
  • Hassan M. Fathallah-Shaykh
    • 3
  • Alan Cantor
    • 4
  • Xiaosi S. Han
    • 3
  • Louis B. Nabors
    • 3
  1. 1.Department of RadiologyThe University of Alabama at BirminghamBirminghamUSA
  2. 2.Department of RadiologyUniversity of WashingtonSeattleUSA
  3. 3.Department of NeurologyThe University of Alabama at BirminghamBirminghamUSA
  4. 4.Department of Preventive MedicineThe University of Alabama at BirminghamBirminghamUSA

Personalised recommendations