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

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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.

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References

  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

    Article  PubMed  CAS  Google Scholar 

  2. 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 10:459–466

    Article  PubMed  CAS  Google Scholar 

  3. Wen PY, Kesari S (2008) Malignant gliomas in adults. N Engl J Med 359:492–507

    Article  PubMed  CAS  Google Scholar 

  4. 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 10:153–161

    Article  PubMed  Google Scholar 

  5. Hartmann C, Hentschel B, Simon M et al (2013) Long-term survival in primary glioblastoma with versus without isocitrate dehydrogenase mutations. Clin Cancer Res 19:5146–5157

    Article  PubMed  CAS  Google Scholar 

  6. 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

    Google Scholar 

  7. 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 96:1365–1373

    PubMed  Google Scholar 

  8. 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 36:739–744

    Article  PubMed  PubMed Central  Google Scholar 

  9. Weber M, Henze M, Tuttenberg J et al (2010) Biopsy targeting gliomas: do functional imaging techniques identify similar target areas? Investig Radiol 45:755–768

    Article  Google Scholar 

  10. Weber M, Zoubaa S, Schlieter M et al (2006) Diagnostic performance of spectroscopic and perfusion MRI for distinction of brain tumors. Neurology 66:1899–1906

    Article  PubMed  CAS  Google Scholar 

  11. 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 251:838–845

    Article  PubMed  Google Scholar 

  12. Sottoriva A, Spiteri I, Piccirillo SGM et al (2013) Intratumor heterogeneity in human glioblastoma reflects cancer evolutionary dynamics. Proc Natl Acad Sci USA 110:4009–4014

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  13. Jacobs AH, Kracht LW, Gossmann A et al (2005) Imaging in neurooncology. NeuroRx 2:333–347

    Article  PubMed  PubMed Central  Google Scholar 

  14. Burger PC, Vogel FS, Green SB, Strike TA (1985) Glioblastoma multiforme and anaplastic astrocytoma. Pathologic criteria and prognostic implications. Cancer 56:1106–1111

    Article  PubMed  CAS  Google Scholar 

  15. 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 23:513–520

    Article  PubMed  Google Scholar 

  16. 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

    Article  PubMed  CAS  Google Scholar 

  17. 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 20:183–184

    Article  PubMed  CAS  Google Scholar 

  18. Maes F, Collignon A, Vandermeulen D, Marchal G, Suetens P (1997) Multimodality image registration by maximization of mutual information. IEEE Trans Med Imaging 16:187–198

    Article  PubMed  CAS  Google Scholar 

  19. 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 1:195–206

    Article  PubMed  CAS  Google Scholar 

  20. Pluim JPW, Maintz JBA, Viergever MA (2003) Mutual-information-based registration of medical images: a survey. IEEE Trans Med Imaging 22:986–1004

    Article  PubMed  Google Scholar 

  21. 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 32:774–782

    Article  PubMed  Google Scholar 

  22. 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 7:134–153

    Article  PubMed  CAS  PubMed Central  Google Scholar 

  23. 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 30:793–802

    PubMed  CAS  Google Scholar 

  24. 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 86:1221–1232

    Article  PubMed  CAS  Google Scholar 

  25. Giese A, Loo MA, Tran N, Haskett D, Coons SW, Berens ME (1996) Dichotomy of astrocytoma migration and proliferation. Int J Cancer 67:275–282

    Article  PubMed  CAS  Google Scholar 

  26. St Croix B, Kerbel RS (1997) Cell adhesion and drug resistance in cancer. Curr Opin Oncol 9:549–556

    Article  PubMed  CAS  Google Scholar 

  27. 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. Rosset A, Spadola L, Ratib O (2004) OsiriX: an open-source software for navigating in multidimensional DICOM images. J Digit Imaging 17:205–216

    Article  PubMed  PubMed Central  Google Scholar 

  29. Schneider CA, Rasband WS, Eliceiri KW (2012) NIH Image to ImageJ: 25 years of image analysis. Nat Methods 9:671–675

    Article  PubMed  CAS  Google Scholar 

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Authors’ contributions

KD: Study concept, Study design, Literature research, Clinical studies, Data acquisition, Data analysis, Statistical analysis, Manuscript preparation. BW: Clinical studies, Data acquisition, Data analysis, Literature Research, Manuscript editing. MG: Data analysis, Manuscript editing. CR: Literature Research. ROF: Data analysis, Manuscript editing. PB: Manuscript editing. PK: Manuscript editing. SH: Manuscript editing. HPS: Manuscript editing. WW: Manuscript editing. MB: Manuscript editing. AR: Study concept, Study design, Literature research, Clinical studies, Data acquisition, Data analysis, Statistical analysis, Manuscript preparation, Guarantor of the integrity of the entire study, Study coordination.

Funding

This study was supported by Guerbet (Paris) and by “Intramurales Förderprogramm”, German Cancer Research Center, DKFZ, Heidelberg.

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    Authors and Affiliations

    1. Neurological University Clinic, Department of Neuroradiology, University of Heidelberg Medical Center, Im Neuenheimer Feld 400, 69120, Heidelberg, Germany

      Katerina Deike, Caroline Reimer, Philipp Bäumer, Philipp Kickingereder, Sabine Heiland, Martin Bendszus & Alexander Radbruch

    2. Department of Neurooncology, University of Heidelberg Medical Center, Heidelberg, Germany

      Benedikt Wiestler & Wolfgang Wick

    3. DKTK, Clinical Cooperation Unit Neurooncology, German Cancer Research Center, DKFZ, Heidelberg, Germany

      Benedikt Wiestler & Wolfgang Wick

    4. Department of Radiology, German Cancer Research Center, DKFZ, Heidelberg, Germany

      Markus Graf, Ralf O. Floca & Heinz-Peter Schlemmer

    5. Neurooncologic Imaging (E012), Department of Radiology, German Cancer Research Center, DKFZ, Heidelberg, Germany

      Alexander Radbruch

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    1. Katerina Deike
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    2. Benedikt Wiestler
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    Correspondence to Alexander Radbruch.

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    The authors indicate no potential conflicts of interest.

    Additional information

    Katerina Deike and Benedikt Wiestler contributed equally to the manuscript.

    Statistical analysis was conducted by Katerina Deike and Alexander Radbruch.

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    Deike, K., Wiestler, B., Graf, M. et al. Prognostic value of combined visualization of MR diffusion and perfusion maps in glioblastoma. J Neurooncol 126, 463–472 (2016). https://doi.org/10.1007/s11060-015-1982-z

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    • DOI: https://doi.org/10.1007/s11060-015-1982-z

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