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Quantitative MR perfusion parameters related to survival time in high-grade gliomas

  • Oncology
  • Published:
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Abstract

Objectives

To evaluate the quantitative parameters obtained from dynamic MR T2*-weighted images as predictors of survival taking into consideration the biasing effects of other survival-related covariates.

Methods

Thirty-nine patients (60 ± 14 years; survival 267 ± 191 days) with high-grade gliomas (8 grade III, 31 grade IV) were retrospectively included in the study. Additional data incorporated Karnofsky performance scale, tumour resection extension after surgery and type of treatment. Dynamic T2*-weighted MRI was acquired before treatment. Tumour curves were extracted for each voxel, and several quantitative parameters were obtained from the whole tumour volume and the 10 % maximum values. Additional image covariates included the presence of necrosis, single or multiple lesions, and tumour and oedema volumes. The relationship between quantitative parameters and survival was assessed using clusterisation techniques and the log-rank method. Cox regression analysis was used to evaluate each parameter’s predictive value.

Results

Only the mean of the 10 % maximum values of the transfer coefficient showed an independent relationship with patient survival (log-rank chi-squared test <0.001, Cox regression P = 0.015), with higher values corresponding to lower survival rates.

Conclusions

High maximum transfer coefficient values show an independent statistical relationship with low survival in high-grade glioma patients. This imaging biomarker can be used as a predictor of prognosis.

Key Points

• Histological examination is the standard procedure for predicting glioma biological behaviour.

• Tumour biopsies may be biased by sample size and location.

• Dynamic T2*-weighted MRI quantitative analysis characterises tumour vasculature at the voxel level.

• High-transfer constant maximum values are independent predictors of low overall survival.

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References

  1. Folkerth RD (2004) Histologic measures of angiogenesis in human primary brain tumors. Cancer Treat Res 117:79–95

    Article  PubMed  Google Scholar 

  2. Burger P (1986) Malignant astrocytic neoplasms: classification, pathology, anatomy, and response to therapy. Semin Oncol 13:16–20

    PubMed  CAS  Google Scholar 

  3. Daumas-Duport C, Scheithauer B, O’Fallon J, Kelly P (1988) Grading of astrocytomas: a simple and reproducible method. Cancer 62:2152–2165

    Article  PubMed  CAS  Google Scholar 

  4. Kleihues P, Burger PC, Scheithauer BW (1993) The new WHO classification of brain tumors. Brain Pathol 3:255–268

    Article  PubMed  CAS  Google Scholar 

  5. Kleihues P, Cavenee WK (2000) World Health Organization classification of tumors: Pathology and genetics of tumors of the nervous system. IARC Press, Lyon

    Google Scholar 

  6. Brat DJ, Parisi JE, Kleinschmidt-DeMasters BK et al (2008) Surgical neuropathology update: a review of changes introduced by the WHO classification of tumours of the central nervous system, 4th edition. Arch Pathol Lab Med 132:993–1007

    PubMed  Google Scholar 

  7. Kepes JJ (1994) Pitfalls and problems in the histopathologic evaluation of stereotactic needle biopsy specimens. Neurosurg Clin N Am 5:19–33

    PubMed  CAS  Google Scholar 

  8. Sawaya R, Hammoud M, Schoppa D et al (1998) Neurosurgical outcomes in a modern series of 400 craniotomies for treatment of parenchymal tumors. Neurosurgery 42:1044–1055

    Article  PubMed  CAS  Google Scholar 

  9. Waldman AD, Jackson A, Price SJ et al (2009) Quantitative imaging biomarkers in neuro-oncology. Nat Rev Clin Oncol 6:445–454

    Article  PubMed  CAS  Google Scholar 

  10. 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 

  11. Ostergaard L, Weisskoff RM, Chesler DA et al (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

    Article  PubMed  CAS  Google Scholar 

  12. Ostergaard L, Sorensen AG, Kwong KK et al (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

    Article  PubMed  CAS  Google Scholar 

  13. Aronen HJ, Gazit IE, Louis DN et al (1994) Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191:41–51

    PubMed  CAS  Google Scholar 

  14. Knopp EA, Cha S, Johnson G et al (1999) Glial neoplasms: dynamic contrast-enhanced T2*-weighted MR imaging. Radiology 211:791–798

    Article  PubMed  CAS  Google Scholar 

  15. Sourbron SP, Buckley DL (2012) Tracer kinetic modeling in MRI: estimating perfusion and capillary permeability. Phys Med Biol 57:1–33

    Article  Google Scholar 

  16. Li KL, Zhu XP, Waterton J et al (2000) Improved 3D quantitative mapping of blood volume and endothelial permeability in brain tumors. J Magn Reson Imaging 12:347–357

    Article  PubMed  CAS  Google Scholar 

  17. Law M, Yang S, Babb 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 

  18. Law M, Young R, Babb J et al (2006) Comparing perfusion metrics obtained from a single compartment versus pharmacokinetic modeling methods using dynamic susceptibility contrast-enhanced perfusion MR imaging with glioma grade. AJNR Am J Neuroradiol 27:1975–1978

    PubMed  CAS  Google Scholar 

  19. 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 

  20. Mills SJ, Patankar TA, Haroon HA et al (2006) Do cerebral blood volume and contrast transfer coefficient predict prognosis in human glioma? AJNR Am J Neuroradiol 27:853–858

    PubMed  CAS  Google Scholar 

  21. 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  Google Scholar 

  22. 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

    Article  PubMed  CAS  Google Scholar 

  23. Albert FK, Forsting M, Sartor K, Adams HP, Kunze S (1994) Early postoperative magnetic resonance imaging after resection of malignant glioma: objective evaluation of residual tumor and its influence on regrowth and prognosis. Neurosurgery 34:45–60

    Article  PubMed  CAS  Google Scholar 

  24. 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 

  25. Lev M, Ozsunar Y, Henson J et al (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. AJNR Am J Neuroradiol 25:214–221

    PubMed  Google Scholar 

  26. Calamante F, Vonken EPA, Van Osch MJP (2007) Contrast agent concentration measurements affecting quantification of bolus tracking perfusion MRI. Magn Reson Med 58:544–553

    Article  PubMed  Google Scholar 

  27. Wong JC, Provenzale JM, Petrella JR (2000) Perfusion MR imaging of brain neoplasms. AJR Am J Roentgenol 174:1147–1157

    Article  PubMed  CAS  Google Scholar 

  28. Revert Ventura AJ, Sanz-Requena R, Martí-Bonmatí L et al (2010) Nosological analysis of MRI tissue perfusion parameters obtained using the unicompartmental and pharmacokinetic models in cerebral glioblastomas. Radiologia 52:432–441

    Article  PubMed  CAS  Google Scholar 

  29. Zhang T, Ramakrishnon R, Livny (1996) BIRCH: an efficient data clustering method for very large databases. Proceedings of the ACM SIGMOD Conference on Management of Data, Montreal, Canada, pp 103-104

  30. Scott CB, Scarantino C, Urtasun R et al (1998) Validation and predictive power of Radiation Therapy Oncology Group (RTOG) recursive partitioning analysis classes for malignant glioma patients: a report using RTOG90–06. Int J Radiat Oncol Biol Phys 40:51–55

    Article  PubMed  CAS  Google Scholar 

  31. Bauman G, Lote K, Larson D et al (1999) Pretreatment factors predict overall survival for patients with low-grade glioma: a recursive partitioning analysis. Int J Radiat Oncol Biol Phys 45:923–929

    Article  PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  33. Pierallini A, Bonamini M, Pantano P et al (1998) Radiological assessment of necrosis in glioblastoma: variability and prognostic value. Neuroradiology 40:150–153

    Article  PubMed  CAS  Google Scholar 

  34. Sugahara T, Korogi Y, Kochi M et al (1999) Usefulness of diffusion-weighted MRI with echo-planar technique in the evaluation of cellularity in gliomas. J Magn Reson Imaging 9:53–60

    Article  PubMed  CAS  Google Scholar 

  35. Shin JH, Lee HK, Kwun BD et al (2002) Using relative cerebral blood flow and volume to evaluate the histopathologic grade of cerebral gliomas: preliminary results. AJR Am J Roentgenol 179:783–789

    Article  PubMed  Google Scholar 

  36. Bulakbasi N, Kocaoglu M, Farzaliyev A et al (2005) Assessment of diagnostic accuracy of perfusion MR imaging in primary and metastatic solitary malignant brain tumors. AJNR Am J Neuroradiol 26:2187–2199

    PubMed  Google Scholar 

  37. Roberts HC, Roberts TPL, Brasch RC et al (2000) Quantitative measurement of microvascular permeability in human brain tumors achieved using dynamic contrast-enhanced MR imaging: correlation with histologic grade. AJNR Am J Neuroradiol 21:891–899

    PubMed  CAS  Google Scholar 

  38. Ludemann L, Gieger W, Wurm R et al (2001) Comparison of dynamic contrast-enhanced MRI with WHO tumor grading for gliomas. Eur Radiol 11:1231–1241

    Article  PubMed  CAS  Google Scholar 

  39. Provenzale JM, Wang GR, Brenner T et al (2002) Comparison of permeability in high-grade and low-grade brain tumors using dynamic susceptibility contrast MR Imaging. AJR Am J Roentgenol 178:711–716

    Article  PubMed  Google Scholar 

  40. Patankar TF, Haroon HA, Mills SJ et al (2005) Is volume transfer coefficient K(trans) related to histologic grade in human gliomas? AJNR Am J Neuroradiol 26:2455–2465

    PubMed  Google Scholar 

  41. Leon SP, Folkerth RD, Black PM (1996) Microvessel density is a prognostic indicator for patients with astroglial brain tumors. Cancer 77:362–372

    Article  PubMed  CAS  Google Scholar 

  42. Abdulrauf SI, Edvardsen K, Ho KL et al (1998) Vascular endothelial growth factor expression and vascular density as prognostic markers of survival in patients with low-grade astrocytomas. J Neurosurg 88:513–520

    Article  PubMed  CAS  Google Scholar 

  43. 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

    PubMed  CAS  Google Scholar 

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

    PubMed  CAS  Google Scholar 

  45. Jain R, Narang J, Griffith B et al (2013) Prognostic vascular imaging biomarkers in high-grade gliomas: tumor permeability as an adjunct to blood volume estimates. Acad Radiol 20:478–485

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Radiology Department, Hospital Quirón Valencia, Av Blasco Ibañez 14, 46010, Valencia, Spain

    Roberto Sanz-Requena, Luis Martí-Bonmatí, Ángel Alberich-Bayarri & Gracián García-Martí

  2. Radiology Department, Hospital de Manises, Av Generalitat Valenciana 50, 46940, Manises, Spain

    Antonio Revert-Ventura

  3. Medical Imaging Area, Hospital Universitari I Politècnic La Fe, Bulevar Sur s/n, 46026, Valencia, Spain

    Luis Martí-Bonmatí

  4. CIBER-SAM, Instituto de Salud Carlos III, Calle Doctor Esquerdo 46, 28007, Madrid, Spain

    Gracián García-Martí

Authors
  1. Roberto Sanz-Requena
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  2. Antonio Revert-Ventura
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  3. Luis Martí-Bonmatí
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  4. Ángel Alberich-Bayarri
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  5. Gracián García-Martí
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Correspondence to Roberto Sanz-Requena.

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Sanz-Requena, R., Revert-Ventura, A., Martí-Bonmatí, L. et al. Quantitative MR perfusion parameters related to survival time in high-grade gliomas. Eur Radiol 23, 3456–3465 (2013). https://doi.org/10.1007/s00330-013-2967-y

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  • DOI: https://doi.org/10.1007/s00330-013-2967-y

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