Post-treatment changes of tumour perfusion parameters can help to predict survival in patients with high-grade astrocytoma



Vascular characteristics of tumour and peritumoral volumes of high-grade gliomas change with treatment. This work evaluates the variations of T2*-weighted perfusion parameters as overall survival (OS) predictors.


Forty-five patients with histologically confirmed high-grade astrocytoma (8 grade III and 37 grade IV) were included. All patients underwent pre- and post-treatment T2*-weighted contrast-enhanced magnetic resonance (MR) imaging. Tumour, peritumoral and control volumes were segmented. Relative variations of cerebral blood flow (CBF), cerebral blood volume (CBV), mean transit time (MTT), Ktrans-T2*, kep-T2*, ve-T2* and vp-T2* were calculated. Differences regarding tumour grade and surgical resection extension were evaluated with ANOVA tests. For each parameter, two groups were defined by non-supervised clusterisation. Survival analysis were performed on these groups.


For the tumour region, the 90th percentile increase or stagnation of CBV was associated with shorter survival, while a decrease related to longer survival (393 ± 189 vs 594 ± 294 days; log-rank p = 0.019; Cox hazard-ratio, 2.31; 95% confidence interval [CI], 1.12-4.74). Ktrans-T2* showed similar results (414 ± 177 vs 553 ± 312 days; log-rank p = 0.037; hazard-ratio, 2.19; 95% CI, 1.03-4.65). The peritumoral area values showed no relationship with OS.


Post-treatment variations of the highest CBV and Ktrans-T2* values in the tumour volume are predictive factors of OS in patients with high-grade gliomas.

Key Points

• Vascular characteristics of high-grade glioma tumour and peritumoral regions change with treatment.

• Quantitative assessment of MRI perfusion provides valuable information regarding tumour aggressiveness.

• Quantitative T2*-weighted perfusion parameters can help to predict overall survival.

• Post-treatment variations of CBV and K trans-T2 values are predictive factors of OS.

• Increased values may justify treatment intensification in these patients.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4



Arterial input function


Cerebral blood flow


Cerebral blood volume


Mean transit time


Overall survival




White matter


  1. 1.

    Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996

    CAS  Article  PubMed  Google Scholar 

  2. 2.

    Chan JL, Lee SW, Fraass BA et al (2002) Survival and failure patterns of high-grade gliomas after 3D conformal radiotherapy. J Clin Oncol 20:1635–1642

    Article  PubMed  Google Scholar 

  3. 3.

    Burger PC, Heinz ER, Shibata T, Kleihues P (1988) Topographic anatomy and CT correlations in the untreated glioblastoma multiforme. J Neurosurg 68:698–704

    CAS  Article  PubMed  Google Scholar 

  4. 4.

    Parsa AT, Wachhorst S, Lamborn KR et al (2005) Prognostic significance of intracranial dissemination of glioblastoma multiforme in adults. J Neurosurg 102:622–628

    Article  PubMed  Google Scholar 

  5. 5.

    Watanabe M, Tanaka R, Takeda N (1992) Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology 34:463–469

    CAS  Article  PubMed  Google Scholar 

  6. 6.

    Folkman J (1971) Tumor angiogenesis: therapeutic implications. N Engl J Med 285:1182–1186

    CAS  Article  PubMed  Google Scholar 

  7. 7.

    Law M, Yang S, Wang H et al (2003) Glioma grading: sensitivity, specificity, and predictive values of perfusion MR imaging and proton MR spectroscopic imaging compared with conventional MR imaging. AJNR Am J Neuroradiol 24:1989–1998

    PubMed  Google Scholar 

  8. 8.

    Lupo JM, Cha S, Chang SM, Nelson SJ (2005) Dynamic susceptibility-weighted perfusion imaging of high-grade gliomas: characterization of spatial heterogeneity. AJNR Am J Neuroradiol 26:1446–1454

    PubMed  Google Scholar 

  9. 9.

    Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 27:475–487

    CAS  PubMed  Google Scholar 

  10. 10.

    Provenzale JM, York G, Moya MG et al (2006) Correlation of relative permeability and relative cerebral blood volume in high-grade cerebral neoplasms. AJR Am J Roentgenol 187:1036–1042

    Article  PubMed  Google Scholar 

  11. 11.

    Emblem KE, Nedregaard B, Nome T et al (2008) Glioma grading by using histogram analysis of blood volume heterogeneity from MR-derived cerebral blood volume maps. Radiology 247:808–817

    Article  PubMed  Google Scholar 

  12. 12.

    Revert Ventura AJ, Sanz-Requena R, Martí-Bonmatí L, Pallardo Y, Jornet J, Gaspar C (2014) The heterogeneity of blood flow on magnetic resonance imaging: a biomarker for grading cerebral astrocytomas. Radiología 56:328–338

    CAS  Article  PubMed  Google Scholar 

  13. 13.

    Mills SJ, Patankar TA, Haroon HA, Balériaux D, Swindell R, Jackson A (2006) Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? AJNR Am J Neuroradiol 27:853–858

    CAS  PubMed  Google Scholar 

  14. 14.

    Law M, Young RJ, Babb JS et al (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

    Article  PubMed  PubMed Central  Google Scholar 

  15. 15.

    Hirai T, Murakami R, Nakamura H et al (2008) Prognostic value of perfusion MR imaging of high-grade astrocytomas: long-term follow-up study. AJNR Am J Neuroradiol 29:1505–1510

    CAS  Article  PubMed  Google Scholar 

  16. 16.

    Sanz-Requena R, Revert-Ventura A, Martí-Bonmatí L, Alberich-Bayarri A, Garcia-Marti G (2013) Quantitative MR perfusion parameters related to survival time in high-grade gliomas. Eur Radiol 23:3456–3465

    Article  PubMed  Google Scholar 

  17. 17.

    Ostergaard L, Weisskoff RM, Chesler DA, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part I: mathematical approach and statistical analysis. Magn Reson Med 36:715–725

    CAS  Article  PubMed  Google Scholar 

  18. 18.

    Ostergaard L, Sorensen AG, Kwong KK, Weisskoff RM, Gyldensted C, Rosen BR (1996) High resolution measurement of cerebral blood flow using intravascular tracer bolus passages. Part II: experimental comparison and preliminary results. Magn Reson Med 36:726–736

    CAS  Article  PubMed  Google Scholar 

  19. 19.

    Law M, Yang S, Babb JS et al (2004) Comparison of cerebral blood volume and vascular permeability from dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 25:746–755

    PubMed  Google Scholar 

  20. 20.

    Johnson G, Wetzel SG, Cha S, Babb J, Tofts PS (2004) Measuring blood volume and vascular transfer constant from dynamic, T2*-weighted contrast-enhanced MRI. J Magn Reson Imaging 51:961–968

    Google Scholar 

  21. 21.

    Henry RG, Vigneron DB, Fischbein NJ et al (2000) Comparison of relative cerebral blood volume and proton spectroscopy in patients with treated gliomas. AJNR Am J Neuroradiol 21:357–366

    CAS  PubMed  Google Scholar 

  22. 22.

    Price SJ, Green HAL, Dean AF, Joseph J, Hutchinson PJ, Gillard JH (2011) Correlation of relative cerebral blood volume with cellularity and proliferation in high grade gliomas: an image-guided biopsy study. AJNR Am J Neuroradiol 32:50–56

    Article  Google Scholar 

  23. 23.

    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-Oncol 103:575–584

    Article  Google Scholar 

  24. 24.

    Jain R, Polsson L, Gutman D et al (2014) Outcome prediction in patients with glioblastoma by using imaging, clinical and genomic biomarkers: focus on the non-enhancing component of the tumor. Radiology 272:484–493

    Article  PubMed  PubMed Central  Google Scholar 

  25. 25.

    Akbari H, Macyszyn L, Da X et al (2014) Pattern analysis of dynamic susceptibility contrast-enhanced MR imaging demonstrates peritumoral tissue heterogeneity. Radiology 273:502–510

    Article  PubMed  PubMed Central  Google Scholar 

  26. 26.

    Cao Y, Tsien CI, Nagesh V et al (2006) Survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT. Int J Radiat Oncol Biol Phys 64:876–885

    Article  PubMed  Google Scholar 

  27. 27.

    Mangla R, Singh G, Ziegelitz D et al (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

    Article  PubMed  Google Scholar 

  28. 28.

    Coban G, Mohan S, Kural F, Wang S, O’Rourke DM, Poptani H (2015) Prognostic value of dynamic susceptibility contrast-enhanced and diffusion-weighted MR imaging in patients with glioblastomas. AJNR Am J Neuroradiol 36:1247–1252

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Jabehdar Maralani P, Melhem ER, Wang S et al (2015) Association of dynamic susceptibility contrast enhanced MR perfusion parameters with prognosis in elderly patients with glioblastomas. Eur Radiol 25:2738–2744

    Article  PubMed  Google Scholar 

  30. 30.

    Bastin ME, Carpenter TK, Armitage PA, Sinha S, Wardlaw JM, Whittle IR (2006) Effects of dexamethasone on cerebral perfusion and water diffusion in patients with high-grade glioma. AJNR Am J Neuroradiol 27:402–408

    CAS  PubMed  Google Scholar 

  31. 31.

    Baek HJ, Kim HS, Kim N, Choi YJ, Kim YJ (2012) Percent change of perfusion skewness and kurtosis: a potential imaging biomarker for early treatment response in patients with newly diagnosed glioblastomas. Radiology 264:834–843

    Article  PubMed  Google Scholar 

  32. 32.

    Law M, Young R, Babb J, Pollack E, Johnson G (2007) Histogram analysis versus region of interest analysis of dynamic susceptibility contrast perfusion MR imaging data in the grading of cerebral gliomas. AJNR Am J Neuroradiol 28:761–766

    CAS  PubMed  Google Scholar 

  33. 33.

    De Wit MC, de Bruin HG, Eijkenboom W, Sillevis Smitt PA, van den Bent MJ (2004) Immediate post-therapy changes in malignant glioma can mimic tumor progression. Neurology 63:535–537

    Article  PubMed  Google Scholar 

  34. 34.

    Cao Y, Nagesh V, Hamstra D et al (2006) The extent and severity of vascular leakage as evidence of tumor aggressiveness in high-grade gliomas. Cancer Res 66:8912–8917

    CAS  Article  PubMed  Google Scholar 

  35. 35.

    Bag AK, Cezayirli PC, Davenport JJ et al (2014) Survival analysis in patients with newly diagnosed primary glioblastoma multiforme using pre- and post-treatment peritumoral perfusion imaging parameters. J Neuro-Oncol 120:361–370

    Article  Google Scholar 

  36. 36.

    Jackson JR, Fuller GN, Abi-Said D et al (2001) Limitations of stereotactic biopsy in the initial management of gliomas. Neuro-Oncology 3:193–200

    CAS  PubMed  PubMed Central  Google Scholar 

  37. 37.

    Pope WB, Sayre J, Perlina A, Villablanca JP, Mischel PS, Cloughesy TF (2005) MR imaging correlates of survival in patients with high-grade gliomas. AJNR Am J Neuroradiol 25:2466–2474

    Google Scholar 

Download references


The scientific guarantor of this publication is Dr. Luis Marti-Bonmati.

The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article.

This study has received funding by the Sociedad Española de Radiología (Becas SERAM Industria 2013). No complex statistical methods were necessary for this paper.

Institutional Review Board approval was obtained. Written informed consent was obtained from all subjects (patients) in this study. Methodology: retrospective, diagnostic or prognostic study, multicentre study.

Author information



Corresponding author

Correspondence to Roberto Sanz-Requena.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Sanz-Requena, R., Revert-Ventura, A.J., García-Martí, G. et al. Post-treatment changes of tumour perfusion parameters can help to predict survival in patients with high-grade astrocytoma. Eur Radiol 27, 3392–3400 (2017).

Download citation


  • Brain
  • Neoplasms
  • Perfusion-weighted MRI
  • Survival
  • Quantitative evaluation