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Three-dimensional susceptibility-weighted imaging at 7 T using fractal-based quantitative analysis to grade gliomas

  • Diagnostic Neuroradiology
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Abstract

Introduction

Susceptibility-weighted imaging (SWI) with high- and ultra-high-field magnetic resonance is a very helpful tool for evaluating brain gliomas and intratumoral structures, including microvasculature. Here, we test whether objective quantification of intratumoral SWI patterns by applying fractal analysis can offer reliable indexes capable of differentiating glial tumor grades.

Methods

Thirty-six patients affected by brain gliomas (grades II–IV, according to the WHO classification system) underwent MRI at 7 T using a SWI protocol. All images were collected and analyzed by applying a computer-aided fractal image analysis, which applies the fractal dimension as a measure of geometrical complexity of intratumoral SWI patterns. The results were subsequently statistically correlated to the histopathological tumor grade.

Results

The mean value of the fractal dimension of the intratumoral SWI patterns was 2.086 ± 0.413. We found a trend of higher fractal dimension values in groups of higher histologic grade. The values ranged from a mean value of 1.682 ± 0.278 for grade II gliomas to 2.247 ± 0.358 for grade IV gliomas (p = 0.013); there was an overall statistically significant difference between histopathological groups.

Conclusion

The present study confirms that SWI at 7 T is a useful method for detecting intratumoral vascular architecture of brain gliomas and that SWI pattern quantification by means of fractal dimension offers a potential objective morphometric image biomarker of tumor grade.

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Abbreviations

FD:

Fractal dimension

FDSWI :

Fractal dimension of the intratumoral SWI pattern

WHO:

World Health Organization

References

  1. Jain RK et al (2007) Angiogenesis in brain tumors. Nat Rev Neurosci 8:610–622

    Article  PubMed  CAS  Google Scholar 

  2. Louis DN et al (2007) The 2007 WHO classification of tumours of the central nervous system. Acta Neuropathol 114:97–109

    Article  PubMed  Google Scholar 

  3. Di Ieva A et al (2011) Angioarchitectural heterogeneity in human glioblastoma multiforme: a fractal-based histopathological assessment. Microvasc Res 81:222–230

    Article  PubMed  Google Scholar 

  4. Folkert RD (2000) Descriptive analysis and quantification of angiogenesis in human brain tumors. J Neurooncol 50:165–172

    Article  Google Scholar 

  5. Birner P et al (2003) Vascular patterns in glioblastoma influence clinical outcome and associate with variable expression of angiogenic proteins: evidence for distinct angiogenic subtypes. Brain Pathol 13:133–143

    Article  PubMed  CAS  Google Scholar 

  6. Van den Bent MJ (2010) Intraobserver variation of the histopathological diagnosis in clinical trials on gliomas: a clinician's perspective. Acta Neuropathol 120:297–304

    Article  PubMed  Google Scholar 

  7. Essig M et al (2011) MR imaging of neoplastic central nervous system lesions: review and recommendations for current practice. AJNR Am J Neuroradiol. doi:10.3174/ajnr.A2640

  8. Robinson RJ, Bhuta S (2011) Susceptibility-weighted imaging of the brain: current utility and potential applications. J Neuroimaging 21:189–204

    Article  Google Scholar 

  9. Haacke EM et al (2004) Susceptibility weighted imaging (SWI). Magn Reson Med 52:612–618

    Article  PubMed  Google Scholar 

  10. Li C et al (2009) Susceptibility-weighted imaging in grading brain astrocytomas. Eur J Radiol 75:87–95

    PubMed  Google Scholar 

  11. Rauscher A et al (2005) Magnetic susceptibility-weighted MR phase imaging of the human brain. AJNR Am J Neuroradiol 26:737–742

    Google Scholar 

  12. Rauscher A et al (2005) High resolution susceptibility weighted MR-imaging of brain tumors during the application of a gaseous agent. Rofo 177:1065–1069

    Article  PubMed  CAS  Google Scholar 

  13. Rauscher A et al (2005) Noninvasive assessment of vascular architecture and function during modulated blood oxygenation using susceptibility weighted magnetic resonance imaging. Magn Reson Med 54:87–95

    Article  PubMed  Google Scholar 

  14. Moenninghoff C et al (2010) Imaging of adult astrocytic brain tumors with 7T MRI: preliminary results. Eur Radiol 20:704–713

    Article  PubMed  Google Scholar 

  15. Pinker K et al (2007) High-resolution contrast-enhanced, susceptibility-weighted MR imaging at 3 T in patients with brain tumors: correlation with positron-emission tomography and histopathologic findings. AJNR Am J Neuroradiol 28:1280–1286

    Article  PubMed  CAS  Google Scholar 

  16. Hori M et al (2010) Precontrast and postcontrast susceptibility-weighted imaging in the assessment of intracranial brain neoplasms at 1.5 T. Jpn J Radiol 28:299–304

    Article  PubMed  Google Scholar 

  17. Hori M et al (2010) Three-dimensional susceptibility-weighted imaging at 3T using various image analysis methods in the estimation of grading intracranial gliomas. Magn Reson Med 28:594–598

    Google Scholar 

  18. Kim HS et al (2009) Added value and diagnostic performance of intratumoral susceptibility signals in the differential diagnosis of solitary enhancing brain lesions: preliminary study. AJNR Am J Neuroradiol 30:1574–1579

    Article  PubMed  CAS  Google Scholar 

  19. Theysohn JM et al (2008) Subjective acceptance of 7 Tesla MRI for human imaging. MAGMA 21:63–72

    Article  PubMed  Google Scholar 

  20. Di Ieva A et al (2012) Fractal analysis of the susceptibility weighted imaging patterns in malignant brain tumors during antiangiogenic treatment: technical report on four cases serially imaged by 7 T magnetic resonance during a period of four weeks. World Neurosurg. doi:10.1016/jwneu.2011.09.006

  21. Losa GA (2009) The fractal geometry of life. Riv Biol 102:29–59

    PubMed  Google Scholar 

  22. Cho ZH et al (2008) Observation of the lenticulostriate arteries in the human brain in vivo using 7.0 T MR angiography. Stroke 39:1604–1606

    Article  PubMed  Google Scholar 

  23. Conijn MM et al (2009) Perforating arteries originating from the posterior communicating artery: a 7.0-Tesla MRI study. Eur Radiol 19:2986–2992

    Article  PubMed  Google Scholar 

  24. Lupo JM et al (2009) GRAPPA-based susceptibility-weighted imaging of normal volunteers and patients with brain tumor at 7 T. Magn Reson Imaging 27:480–488

    Article  PubMed  Google Scholar 

  25. von Morze C et al (2007) Intracranial time-of-flight MR angiography at 7 T with comparisons to 3 T. J Mag Reson Imaging 26:900–904

    Article  Google Scholar 

  26. Di Ieva A et al (2011) The veins of the nucleus dentatus: anatomical and radiological findings. NeuroImage 54:74–79

    Article  PubMed  Google Scholar 

  27. Grabner G et al (2012) Longitudinal brain imaging of five malignant glioma patients treated with bevacizumab using susceptibility-weighted magnetic resonance imaging at 7 T. Magn Reson Imaging 30:139–147

    Article  PubMed  CAS  Google Scholar 

  28. Moser E et al (2012) T MR-from research to clinical applications? NMR Biomed 25:695–716

    Article  PubMed  Google Scholar 

  29. Sehgal V et al (2006) Susceptibility-weighted imaging to visualize blood products and improve tumor contrast in the study of brain masses. J Magn Reson Imaging 24:41–51

    Article  PubMed  Google Scholar 

  30. Zhang W et al (2010) Application of susceptibility weighted imaging in revealing intratumoral blood products and grading gliomas. J Radiol 91:485–490

    Article  PubMed  CAS  Google Scholar 

  31. Di Ieva A (2010) Angioarchitectural morphometrics of brain tumors: are there any potential histopathological biomarkers? Microvasc Res 80:522–533

    Article  PubMed  Google Scholar 

  32. Doblas S et al (2010) Glioma morphology and tumor-induced vascular alterations revealed in 7 rodent glioma models by in vivo magnetic resonance imaging and angiography. J Mag Reson Imaging 32:267–275

    Article  Google Scholar 

  33. Jakab A et al (2011) Glioma grade assessment by using histogram analysis of diffusion tensor imaging-derived maps. Neuroradiology 53:483–491

    Article  PubMed  Google Scholar 

Download references

Acknowledgments

ADI received a 2010 research grant from the Italian Society of Neurosurgery (SINch) and a 2011 Aesculap European Association of Neurosurgical Societies Laboratory Research Prize. This study was supported by the Jubiläumsfonds of the Austrian National Bank (grant no. 13457). The authors wish to thank FMEA (the Society for the Promotion of Research in Microsurgical and Endoscopic Anatomy) for funding the production of this article. We would like to give special thanks to the Virtual Fractal Lab Team (www.fractal-lab.org) for developing the software used here and for their continued technical support beyond image and fractal analyses.

Conflict of interest

We declare that we have no conflict of interest.

Author information

Authors and Affiliations

  1. Center for Anatomy and Cell Biology, Department of Systematic Anatomy, Medical University of Vienna, Vienna, Austria

    Antonio Di Ieva

  2. Division of Neurosurgery, Department of Surgery, St. Michael’s Hospital, University of Toronto, 30 Bond Street, Toronto, ON, M5B 1W8, Canada

    Antonio Di Ieva

  3. MR Centre of Excellence, Department of Radiology, Medical University of Vienna, Vienna, Austria

    Sabine Göd, Günther Grabner & Siegfrid Trattnig

  4. IRCCS, Istituto Clinico Humanitas, Rozzano, Milan, Italy

    Fabio Grizzi

  5. Department of Neurosurgery, Krankenanstalt Rudolfstiftung, Vienna, Austria

    Camillo Sherif

  6. Department of Neurosurgery, Medical University of Vienna, Vienna, Austria

    Christian Matula

  7. Department of Anatomy, Medical University of Brescia, Brescia, Italy

    Manfred Tschabitscher

Authors
  1. Antonio Di Ieva
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  2. Sabine Göd
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  3. Günther Grabner
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  4. Fabio Grizzi
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  5. Camillo Sherif
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  6. Christian Matula
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  7. Manfred Tschabitscher
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  8. Siegfrid Trattnig
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Corresponding author

Correspondence to Antonio Di Ieva.

Additional information

The preliminary results of this study were presented at the 59th Congress of the Italian Society of Neurosurgery (SINch) in Milan, Italy; the Research Course of the European Association of Neurosurgical Societies (EANS) in Lausanne, Switzerland; and the 16th Congress of the Italian Society of Neurooncology (AINO) in Milan, Italy.

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Di Ieva, A., Göd, S., Grabner, G. et al. Three-dimensional susceptibility-weighted imaging at 7 T using fractal-based quantitative analysis to grade gliomas. Neuroradiology 55, 35–40 (2013). https://doi.org/10.1007/s00234-012-1081-1

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  • DOI: https://doi.org/10.1007/s00234-012-1081-1

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