Skip to main content
Top
Published in: Journal of Neuro-Oncology 3/2016

01-02-2016 | Clinical Study

Prognostic value of combined visualization of MR diffusion and perfusion maps in glioblastoma

Authors: Katerina Deike, Benedikt Wiestler, Markus Graf, Caroline Reimer, Ralf O. Floca, Philipp Bäumer, Philipp Kickingereder, Sabine Heiland, Heinz-Peter Schlemmer, Wolfgang Wick, Martin Bendszus, Alexander Radbruch

Published in: Journal of Neuro-Oncology | Issue 3/2016

Login to get access

Abstract

We analyzed whether the combined visualization of decreased apparent diffusion coefficient (ADC) values and increased cerebral blood volume (CBV) in perfusion imaging can identify prognosis-related growth patterns in patients with newly diagnosed glioblastoma. Sixty-five consecutive patients were examined with diffusion and dynamic susceptibility-weighted contrast-enhanced perfusion weighted MRI. ADC and CBV maps were co-registered on the T1-w image and a region of interest (ROI) was manually delineated encompassing the enhancing lesion. Within this ROI pixels with ADC values <the 30th percentile (ADCmin), pixels with CBV values >the 70th percentile (CBVmax) and the intersection of pixels with ADCmin and CBVmax were automatically calculated and visualized. Initially, all tumors with a mean intersection greater than the upper quartile of the normally distributed mean intersection of all patients were subsumed to the first growth pattern termed big intersection (BI). Subsequently, the remaining tumors’ growth patterns were categorized depending on the qualitative representation of ADCmin, CBVmax and their intersection. Log-rank test exposed a significantly longer overall survival of BI (n = 16) compared to non-BI group (n = 49) (p = 0.0057). Thirty-one, four and 14 patients of the non-BI group were classified as predominant ADC-, CBV- and mixed growth group, respectively. In a multivariate Cox regression model, the BI-, CBV- and mixed groups had significantly lower adjusted hazard ratios (p-value, αBonferroni < 0.006) when compared to the reference group ADC: 0.29 (0.0027), 0.11 (0.038) and 0.33 (0.0059). Our study provides evidence that the combination of diffusion and perfusion imaging allows visualization of different glioblastoma growth patterns that are associated with prognosis. A possible biological hypothesis for this finding could be the interpretation of the ADCmin fraction as the invasion-front of tumor cells while the CBVmax fraction might represent the vascular rich tumor border that is “trailing behind” the invasion-front in the ADC group.
Appendix
Available only for authorised users
Literature
1.
go back to reference Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996PubMedCrossRef Stupp R, Mason WP, van den Bent MJ et al (2005) Radiotherapy plus concomitant and adjuvant temozolomide for glioblastoma. N Engl J Med 352:987–996PubMedCrossRef
2.
go back to reference Stupp R, Hegi ME, Mason WP et al (2009) 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 10:459–466PubMedCrossRef Stupp R, Hegi ME, Mason WP et al (2009) 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 10:459–466PubMedCrossRef
4.
go back to reference Field K, Rosenthal M, Yilmaz M, Tacey M, Drummond KJ (2014) Comparison between poor and long-term survivors with glioblastoma: review of an Australian dataset. Asia Pac J Clin Oncol 10:153–161PubMedCrossRef Field K, Rosenthal M, Yilmaz M, Tacey M, Drummond KJ (2014) Comparison between poor and long-term survivors with glioblastoma: review of an Australian dataset. Asia Pac J Clin Oncol 10:153–161PubMedCrossRef
5.
go back to reference Hartmann C, Hentschel B, Simon M et al (2013) Long-term survival in primary glioblastoma with versus without isocitrate dehydrogenase mutations. Clin Cancer Res 19:5146–5157PubMedCrossRef Hartmann C, Hentschel B, Simon M et al (2013) Long-term survival in primary glioblastoma with versus without isocitrate dehydrogenase mutations. Clin Cancer Res 19:5146–5157PubMedCrossRef
6.
go back to reference Louis DN, Ohgaki H, Wiestler OD, Cavenee WK (eds) (2007) WHO classification of tumours of the central nervous system, 4th edn. IARC, Lyon, pp 33–52 Louis DN, Ohgaki H, Wiestler OD, Cavenee WK (eds) (2007) WHO classification of tumours of the central nervous system, 4th edn. IARC, Lyon, pp 33–52
7.
go back to reference Chawalparit O, Sangruchi T, Witthiwej T et al (2013) Diagnostic performance of advanced MRI in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 96:1365–1373PubMed Chawalparit O, Sangruchi T, Witthiwej T et al (2013) Diagnostic performance of advanced MRI in differentiating high-grade from low-grade gliomas in a setting of routine service. J Med Assoc Thai 96:1365–1373PubMed
8.
go back to reference Wang S, Zhou J (2012) Diffusion tensor magnetic resonance imaging of rat glioma models: a correlation study of MR imaging and histology. J Comput Assist Tomogr 36:739–744PubMedPubMedCentralCrossRef Wang S, Zhou J (2012) Diffusion tensor magnetic resonance imaging of rat glioma models: a correlation study of MR imaging and histology. J Comput Assist Tomogr 36:739–744PubMedPubMedCentralCrossRef
9.
go back to reference Weber M, Henze M, Tuttenberg J et al (2010) Biopsy targeting gliomas: do functional imaging techniques identify similar target areas? Investig Radiol 45:755–768CrossRef Weber M, Henze M, Tuttenberg J et al (2010) Biopsy targeting gliomas: do functional imaging techniques identify similar target areas? Investig Radiol 45:755–768CrossRef
10.
go back to reference Weber M, Zoubaa S, Schlieter M et al (2006) Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology 66:1899–1906PubMedCrossRef Weber M, Zoubaa S, Schlieter M et al (2006) Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology 66:1899–1906PubMedCrossRef
11.
go back to reference Murakami R, Hirai T, Sugahara T et al (2009) Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one- versus two-parameter pilot method. Radiology 251:838–845PubMedCrossRef Murakami R, Hirai T, Sugahara T et al (2009) Grading astrocytic tumors by using apparent diffusion coefficient parameters: superiority of a one- versus two-parameter pilot method. Radiology 251:838–845PubMedCrossRef
12.
go back to reference Sottoriva A, Spiteri I, Piccirillo SGM et al (2013) Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 110:4009–4014PubMedPubMedCentralCrossRef Sottoriva A, Spiteri I, Piccirillo SGM et al (2013) Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 110:4009–4014PubMedPubMedCentralCrossRef
14.
go back to reference Burger PC, Vogel FS, Green SB, Strike TA (1985) Glioblastoma multiforme and anaplastic astrocytoma. Pathologic criteria and prognostic implications. Cancer 56:1106–1111PubMedCrossRef Burger PC, Vogel FS, Green SB, Strike TA (1985) Glioblastoma multiforme and anaplastic astrocytoma. Pathologic criteria and prognostic implications. Cancer 56:1106–1111PubMedCrossRef
15.
go back to reference Romano A, Calabria LF, Tavanti F et al (2013) Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: correlation with MGMT promoter methylation status. Eur Radiol 23:513–520PubMedCrossRef Romano A, Calabria LF, Tavanti F et al (2013) Apparent diffusion coefficient obtained by magnetic resonance imaging as a prognostic marker in glioblastomas: correlation with MGMT promoter methylation status. Eur Radiol 23:513–520PubMedCrossRef
16.
go back to reference Aronen HJ, Gazit IE, Louis DN et al (1994) Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191:41–51PubMedCrossRef Aronen HJ, Gazit IE, Louis DN et al (1994) Cerebral blood volume maps of gliomas: comparison with tumor grade and histologic findings. Radiology 191:41–51PubMedCrossRef
17.
go back to reference Radbruch A, Bendszus M, Wick W, Heiland S (2010) Comment to: parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma: pitfalls in perfusion MRI in brain tumors : Tsien C, Galban CJ, Chenevert TL, Johnson TD, Hamstra DA, Sundgren PC, Junck L. Clin Neuroradiol 20:183–184PubMedCrossRef Radbruch A, Bendszus M, Wick W, Heiland S (2010) Comment to: parametric response map as an imaging biomarker to distinguish progression from pseudoprogression in high-grade glioma: pitfalls in perfusion MRI in brain tumors : Tsien C, Galban CJ, Chenevert TL, Johnson TD, Hamstra DA, Sundgren PC, Junck L. Clin Neuroradiol 20:183–184PubMedCrossRef
18.
go back to reference Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16:187–198PubMedCrossRef Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16:187–198PubMedCrossRef
19.
go back to reference Meyer CR, Boes JL, Kim B et al (1997) Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Med Image Anal 1:195–206PubMedCrossRef Meyer CR, Boes JL, Kim B et al (1997) Demonstration of accuracy and clinical versatility of mutual information for automatic multimodality image fusion using affine and thin-plate spline warped geometric deformations. Med Image Anal 1:195–206PubMedCrossRef
20.
go back to reference Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986–1004PubMedCrossRef Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986–1004PubMedCrossRef
21.
go back to reference Marko NF, Weil RJ, Schroeder JL, Lang FF, Suki D, Sawaya RE (2014) Extent of resection of glioblastoma revisited: personalized survival modeling facilitates more accurate survival prediction and supports a maximum-safe-resection approach to surgery. J Clin Oncol 32:774–782PubMedCrossRef Marko NF, Weil RJ, Schroeder JL, Lang FF, Suki D, Sawaya RE (2014) Extent of resection of glioblastoma revisited: personalized survival modeling facilitates more accurate survival prediction and supports a maximum-safe-resection approach to surgery. J Clin Oncol 32:774–782PubMedCrossRef
22.
go back to reference Kaur B, Khwaja FW, Severson EA, Matheny SL, Brat DJ, Van Meir EG (2005) Hypoxia and the hypoxia-inducible-factor pathway in glioma growth and angiogenesis. Neuro Oncol 7:134–153PubMedPubMedCentralCrossRef Kaur B, Khwaja FW, Severson EA, Matheny SL, Brat DJ, Van Meir EG (2005) Hypoxia and the hypoxia-inducible-factor pathway in glioma growth and angiogenesis. Neuro Oncol 7:134–153PubMedPubMedCentralCrossRef
23.
go back to reference Fujiwara S, Nakagawa KOU, Harada H et al (2007) Silencing hypoxia-inducible factor-1 · inhibits cell migration and invasion under hypoxic environment in malignant gliomas. Int J Oncol 30:793–802PubMed Fujiwara S, Nakagawa KOU, Harada H et al (2007) Silencing hypoxia-inducible factor-1 · inhibits cell migration and invasion under hypoxic environment in malignant gliomas. Int J Oncol 30:793–802PubMed
24.
go back to reference Zagzag D, Lukyanov Y, Lan L et al (2006) Hypoxia-inducible factor 1 and VEGF upregulate CXCR4 in glioblastoma: implications for angiogenesis and glioma cell invasion. Lab Investig 86:1221–1232PubMedCrossRef Zagzag D, Lukyanov Y, Lan L et al (2006) Hypoxia-inducible factor 1 and VEGF upregulate CXCR4 in glioblastoma: implications for angiogenesis and glioma cell invasion. Lab Investig 86:1221–1232PubMedCrossRef
25.
go back to reference Giese A, Loo MA, Tran N, Haskett D, Coons SW, Berens ME (1996) Dichotomy of astrocytoma migration and proliferation. Int J Cancer 67:275–282PubMedCrossRef Giese A, Loo MA, Tran N, Haskett D, Coons SW, Berens ME (1996) Dichotomy of astrocytoma migration and proliferation. Int J Cancer 67:275–282PubMedCrossRef
26.
go back to reference St Croix B, Kerbel RS (1997) Cell adhesion and drug resistance in cancer. Curr Opin Oncol 9:549–556PubMedCrossRef St Croix B, Kerbel RS (1997) Cell adhesion and drug resistance in cancer. Curr Opin Oncol 9:549–556PubMedCrossRef
27.
go back to reference McAuliffe MJ, Lalonde FM, McGarry D, Gandler W, Csaky K, Trus BL (2001) Medical image processing, analysis & visualization in clinical research. Computer-Based Medical Systems, pp 381–386 McAuliffe MJ, Lalonde FM, McGarry D, Gandler W, Csaky K, Trus BL (2001) Medical image processing, analysis & visualization in clinical research. Computer-Based Medical Systems, pp 381–386
28.
29.
go back to reference Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675PubMedCrossRef Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675PubMedCrossRef
Metadata
Title
Prognostic value of combined visualization of MR diffusion and perfusion maps in glioblastoma
Authors
Katerina Deike
Benedikt Wiestler
Markus Graf
Caroline Reimer
Ralf O. Floca
Philipp Bäumer
Philipp Kickingereder
Sabine Heiland
Heinz-Peter Schlemmer
Wolfgang Wick
Martin Bendszus
Alexander Radbruch
Publication date
01-02-2016
Publisher
Springer US
Published in
Journal of Neuro-Oncology / Issue 3/2016
Print ISSN: 0167-594X
Electronic ISSN: 1573-7373
DOI
https://doi.org/10.1007/s11060-015-1982-z

Other articles of this Issue 3/2016

Journal of Neuro-Oncology 3/2016 Go to the issue