Advertisement

European Radiology

, Volume 27, Issue 8, pp 3392–3400 | Cite as

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

  • Roberto Sanz-Requena
  • Antonio J. Revert-Ventura
  • Gracián García-Martí
  • Fares Salamé-Gamarra
  • Alexandre Pérez-Girbés
  • Enrique Mollá-Olmos
  • Luis Martí-Bonmatí
Oncology

Abstract

Objectives

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.

Methods

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.

Results

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.

Conclusions

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.

Keywords

Brain Neoplasms Perfusion-weighted MRI Survival Quantitative evaluation 

Abbreviations

AIF

Arterial input function

CBF

Cerebral blood flow

CBV

Cerebral blood volume

MTT

Mean transit time

OS

Overall survival

TMZ

Temozolamide

WM

White matter

Notes

Acknowledgements

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.

References

  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 352:987–996CrossRefPubMedGoogle 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 20:1635–1642CrossRefPubMedGoogle Scholar
  3. 3.
    Burger PC, Heinz ER, Shibata T, Kleihues P (1988) Topographic anatomy and CT correlations in the untreated glioblastoma multiforme. J Neurosurg 68:698–704CrossRefPubMedGoogle Scholar
  4. 4.
    Parsa AT, Wachhorst S, Lamborn KR et al (2005) Prognostic significance of intracranial dissemination of glioblastoma multiforme in adults. J Neurosurg 102:622–628CrossRefPubMedGoogle Scholar
  5. 5.
    Watanabe M, Tanaka R, Takeda N (1992) Magnetic resonance imaging and histopathology of cerebral gliomas. Neuroradiology 34:463–469CrossRefPubMedGoogle Scholar
  6. 6.
    Folkman J (1971) Tumor angiogenesis: therapeutic implications. N Engl J Med 285:1182–1186CrossRefPubMedGoogle 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 24:1989–1998PubMedGoogle 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 26:1446–1454PubMedGoogle Scholar
  9. 9.
    Cha S (2006) Update on brain tumor imaging: from anatomy to physiology. AJNR Am J Neuroradiol 27:475–487PubMedGoogle 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 187:1036–1042CrossRefPubMedGoogle 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 247:808–817CrossRefPubMedGoogle 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 56:328–338CrossRefPubMedGoogle 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 27:853–858PubMedGoogle 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 247:490–498CrossRefPubMedPubMedCentralGoogle 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 29:1505–1510CrossRefPubMedGoogle 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 23:3456–3465CrossRefPubMedGoogle 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 36:715–725CrossRefPubMedGoogle 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 36:726–736CrossRefPubMedGoogle 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 25:746–755PubMedGoogle 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 51:961–968Google 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 21:357–366PubMedGoogle 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 32:50–56CrossRefGoogle 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 103:575–584CrossRefGoogle 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 272:484–493CrossRefPubMedPubMedCentralGoogle 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 273:502–510CrossRefPubMedPubMedCentralGoogle 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 64:876–885CrossRefPubMedGoogle 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 256:575–584CrossRefPubMedGoogle 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 36:1247–1252CrossRefPubMedGoogle 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 25:2738–2744CrossRefPubMedGoogle 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 27:402–408PubMedGoogle 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 264:834–843CrossRefPubMedGoogle 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 28:761–766PubMedGoogle 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 63:535–537CrossRefPubMedGoogle 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 66:8912–8917CrossRefPubMedGoogle 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 120:361–370CrossRefGoogle 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 3:193–200PubMedPubMedCentralGoogle 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 25:2466–2474Google Scholar

Copyright information

© European Society of Radiology 2016

Authors and Affiliations

  • Roberto Sanz-Requena
    • 1
    • 2
  • Antonio J. Revert-Ventura
    • 3
  • Gracián García-Martí
    • 1
    • 2
    • 4
  • Fares Salamé-Gamarra
    • 3
  • Alexandre Pérez-Girbés
    • 2
  • Enrique Mollá-Olmos
    • 5
  • Luis Martí-Bonmatí
    • 1
    • 2
  1. 1.Radiology DepartmentHospital Quirónsalud ValenciaValenciaSpain
  2. 2.Grupo de Investigación Biomédica en ImagenHospital Universitari i Politècnic La FeValenciaSpain
  3. 3.Radiology DepartmentHospital de ManisesManisesSpain
  4. 4.CIBER-SAM, Instituto de Salud Carlos IIIMadridSpain
  5. 5.Radiology DepartmentHospital La RiberaAlziraSpain

Personalised recommendations