Advertisement

Computational Fractal-Based Analysis of Brain Tumor Microvascular Networks

  • Antonio Di IevaEmail author
  • Omar S. Al-Kadi
Chapter
Part of the Springer Series in Computational Neuroscience book series (NEUROSCI)

Abstract

Brain parenchyma microvasculature is set in disarray in the presence of tumors, and malignant brain tumors are among the most vascularized neoplasms in humans. As microvessels can be easily identified in histologic specimens, quantification of microvascularity can be used alone or in combination with other histological features to increase the understanding of the dynamic behavior, diagnosis, and prognosis of brain tumors. Different brain tumors, and even subtypes of the same tumor, show specific microvascular patterns, as a kind of “microvascular fingerprint,” which is particular to each histotype. Reliable morphometric parameters are required for the qualitative and quantitative characterization of the neoplastic angioarchitecture, although the lack of standardization of a technique able to quantify the microvascular patterns in an objective way has limited the “morphometric approach” in neuro-oncology.

In this chapter we focus on the importance of the computational-based morphometrics, for the objective description of the tumoral microvascular fingerprinting. By also introducing the concept of “angio-space,” which is the tumoral space occupied by the microvessels, we here present fractal analysis as the most reliable computational tool able to offer objective parameters for the description of the microvascular networks.

The spectrum of different angioarchitectural configurations can be quantified by means of Euclidean and fractal-based parameters in a multiparametric analysis, aimed to offer surrogate biomarkers of cancer. Such parameters are here described from the methodological point of view (i.e., feature extraction) as well as from the clinical perspective (i.e., relation to underlying physiology), in order to offer new computational parameters to the clinicians with the final goal of improving diagnostic and prognostic power of patients affected by brain tumors.

Keywords

Angioarchitecture Brain tumor Fractal dimension Fractal analysis Glioblastoma multiforme Microvascularity 

References

  1. 1.
    Al-Kadi OS. Texture measures combination for improved meningioma classification of histopathological images. Pattern Recog. 2010;43:2043–53.CrossRefGoogle Scholar
  2. 2.
    Al-Kadi OS. A multiresolution clinical decision support system based on fractal model design for classification of histological brain tumours. Comput Med Imaging Graph. 2015;41:67–79.CrossRefPubMedGoogle Scholar
  3. 3.
    Baish JW, Gazit Y, Berk DA, et al. Role of tumor vascular architecture in nutrient and drug delivery: an invasion percolation-based network model. Microvasc Res. 1996;51:327–46.CrossRefPubMedGoogle Scholar
  4. 4.
    Baish JW, Jain RK. Fractals and cancer. Cancer Res. 2000;60:3683–8.PubMedGoogle Scholar
  5. 5.
    Birner P, Piribauer M, Fischer I, et al. Vascular patterns in glioblastoma influence clinical outcome and associate with variable expression of angiogenic proteins: Evidence for distinct angiogenic subtypes. Brain Pathol. 2003;13:133–43.CrossRefPubMedGoogle Scholar
  6. 6.
    Brem S, Cotran R, Folkman J. Tumor angiogenesis: a quantitative method for histologic grading. J Natl Cancer Inst. 1972;48:347–56.PubMedGoogle Scholar
  7. 7.
    Caserta F, Eldred WD, Fernandez E, et al. Determination of fractal dimension of physiologically characterized neurons in two and three dimensions. J Neurosci Methods. 1995;56:133–44.CrossRefPubMedGoogle Scholar
  8. 8.
    Cassot F, Lauwers F, Fouard C, et al. A novel three-dimensional computer-assisted method for a quantitative study of microvascular networks of the human cerebral cortex. Microcirculation. 2006;13:1–18.CrossRefPubMedGoogle Scholar
  9. 9.
    Cross SS, Start RD, Silcocks PB, et al. Quantitation of the renal arterial tree by fractal analysis. J Pathol. 1993;170:479–84.CrossRefPubMedGoogle Scholar
  10. 10.
    Cross SS. The application of fractal geometric analysis to microscopic images. Micron. 1994;25:101–13.CrossRefPubMedGoogle Scholar
  11. 11.
    Di Ieva A, Tschabitscher M. Fractal-based classification of brain tumors angioarchitecture. In: Murray SR, Mitchell EW, editors. Classification and applications of fractals: new research. New York: Nova Science Publishers; 2012. p. 205–16.Google Scholar
  12. 12.
    Di Ieva A, Grizzi F, Ceva-Grimaldi G, et al. Fractal dimension as a quantitator of the microvasculature of normal and adenomatous pituitary tissue. J Anat. 2007;211:673–80.CrossRefPubMedPubMedCentralGoogle Scholar
  13. 13.
    Di Ieva A, Grizzi F, Gaetani P, et al. Euclidean and fractal geometry of microvascular networks in normal and neoplastic pituitary tissue. Neurosurg Rev. 2008;31:271–81.CrossRefPubMedGoogle Scholar
  14. 14.
    Di Ieva A. Angioarchitectural morphometrics of brain tumors: are there any potential histopathological biomarkers? Microvasc Res. 2010;80:522–33.CrossRefPubMedGoogle Scholar
  15. 15.
    Di Ieva A, Grizzi F. Microvessel density. J Neurosurg Pediatr. 2010;6:304–6; author reply 306.CrossRefPubMedGoogle Scholar
  16. 16.
    Di Ieva A, Grizzi F, Ceva-Grimaldi G, et al. The microvascular network of the pituitary gland: a model for the application of fractal geometry to the analysis of angioarchitecture and angiogenesis of brain tumors. J Neurosurg Sci. 2010;54:49–54.PubMedGoogle Scholar
  17. 17.
    Di Ieva A, Grizzi F, Tschabitscher M, et al. Correlation of microvascular fractal dimension with positron emission tomography [(11)C]-methionine uptake in glioblastoma multiforme: preliminary findings. Microvasc Res. 2010;80:267–73.CrossRefPubMedGoogle Scholar
  18. 18.
    Di Ieva A, Grizzi F, Sherif C, et al. Angioarchitectural heterogeneity in human glioblastoma multiforme: a fractal-based histopathological assessment. Microvasc Res. 2011;81:222–30.CrossRefPubMedGoogle Scholar
  19. 19.
    Di Ieva A. Fractal analysis of microvascular networks in malignant brain tumors. Clin Neuropathol. 2012;31:342–51.CrossRefPubMedGoogle Scholar
  20. 20.
    Di Ieva A, Bruner E, Widhalm G, et al. Computer-assisted and fractal-based morphometric assessment of microvascularity in histological specimens of gliomas. Sci Rep. 2012;2:429.PubMedPubMedCentralGoogle Scholar
  21. 21.
    Di Ieva A, Weckman A, Di Michele J, et al. Microvascular morphometrics of the hypophysis and pituitary tumors: from bench to operating theatre. Microvasc Res. 2013;89:7–14.CrossRefPubMedGoogle Scholar
  22. 22.
    Duvernoy HM, Delon S, Vannson JL. Cortical blood vessels of the human brain. Brain Res Bull. 1981;7:519–79.CrossRefPubMedGoogle Scholar
  23. 23.
    Duvernoy HM. Vascularization of the cerebral cortex. Rev Neurol (Paris). 1999;155:684–7.Google Scholar
  24. 24.
    Duvernoy HM. Comments on the microvascularization of the brain. Cerebrovasc Dis. 2006;21:423–4.CrossRefPubMedGoogle Scholar
  25. 25.
    Gazit Y, Baish JW, Safabakhsh N, et al. Fractal characteristics of tumor vascular architecture during tumor growth and regression. Microcirculation. 1997;4:395–402.CrossRefPubMedGoogle Scholar
  26. 26.
    Goldberger AL, West BJ. Fractals in physiology and medicine. Yale J Biol Med. 1987;60:421–35.PubMedPubMedCentralGoogle Scholar
  27. 27.
    Grasman J, Brascamp JW, Van Leeuwen JL, et al. The multifractal structure of arterial trees. J Theor Biol. 2003;220:75–82.CrossRefPubMedGoogle Scholar
  28. 28.
    Grizzi F, Russo C, Colombo P, et al. Quantitative evaluation and modeling of two-dimensional neovascular network complexity: the surface fractal dimension. BMC Cancer. 2005;5:14.CrossRefPubMedPubMedCentralGoogle Scholar
  29. 29.
    Grizzi F, Colombo P, Taverna G, et al. Geometry of human vascular system: is it an obstacle for quantifying antiangiogenic therapies? Appl Immunohistochem Mol Morphol. 2007;15:134–9.CrossRefPubMedGoogle Scholar
  30. 30.
    Hainfellner JA, Heinzl H. Neuropathological biomarker candidates in brain tumors: key issues for translational efficiency. Clin Neuropathol. 2010;29:41–54.CrossRefPubMedGoogle Scholar
  31. 31.
    Hlatky L, Hahnfeldt P, Folkman J. Clinical application of antiangiogenic therapy: microvessel density, what it does and doesn’t tell us. J Natl Cancer Inst. 2002;94:883–93.CrossRefPubMedGoogle Scholar
  32. 32.
    Jain RK. Normalizing tumor vasculature with anti-angiogenic therapy: a new paradigm for combination therapy. Nat Med. 2001;7:987–9.CrossRefPubMedGoogle Scholar
  33. 33.
    Jain RK. Normalization of tumor vasculature: an emerging concept in antiangiogenic therapy. Science. 2005;307:58–62.CrossRefPubMedGoogle Scholar
  34. 34.
    Jain RK, di Tomaso E, Duda DG, et al. Angiogenesis in brain tumours. Nat Rev Neurosci. 2007;8:610–22.CrossRefPubMedGoogle Scholar
  35. 35.
    Kedzia A, Rybaczuk M, Andrzejak R. Fractal dimensions of human brain cortex vessels during the fetal period. Med Sci Monit. 2002;8:MT46–51.PubMedGoogle Scholar
  36. 36.
    Kirchner LM, Schmidt SP, Gruber BS. Quantitation of angiogenesis in the chick chorioallantoic membrane model using fractal analysis. Microvasc Res. 1996;51:2–14.CrossRefPubMedGoogle Scholar
  37. 37.
    Korkolopoulou P, Patsouris E, Kavantzas N, et al. Prognostic implications of microvessel morphometry in diffuse astrocytic neoplasms. Neuropathol Appl Neurobiol. 2002;28:57–66.CrossRefPubMedGoogle Scholar
  38. 38.
    Lauwers F, Cassot F, Lauwers-Cances V, et al. Morphometry of the human cerebral cortex microcirculation: general characteristics and space-related profiles. Neuroimage. 2008;39:936–48.CrossRefPubMedGoogle Scholar
  39. 39.
    Lorthois S, Cassot F. Fractal analysis of vascular networks: insights from morphogenesis. J Theor Biol. 2010;262:614–33.CrossRefPubMedGoogle Scholar
  40. 40.
    Losa GA. The fractal geometry of life. Riv Biol. 2009;102:29–59.PubMedGoogle Scholar
  41. 41.
    Louis DN, Ohgaki H, Wiestler OD, et al. WHO classification of tumours of the central nervous system. Lyon: International Agency for Research on Cancer (IARC); 2007.Google Scholar
  42. 42.
    Mandelbrot BB. The fractal geometry of nature. New York: W.H. Freeman; 1982.Google Scholar
  43. 43.
    Preusser M, Heinzl H, Gelpi E, et al. Histopathologic assessment of hot-spot microvessel density and vascular patterns in glioblastoma: poor observer agreement limits clinical utility as prognostic factors: a translational research project of the European organization for research and treatment of cancer brain tumor group. Cancer. 2006;107:162–70.CrossRefPubMedGoogle Scholar
  44. 44.
    Risser L, Plouraboue F, Steyer A, et al. From homogeneous to fractal normal and tumorous microvascular networks in the brain. J Cereb Blood Flow Metab. 2007;27:293–303.CrossRefPubMedGoogle Scholar
  45. 45.
    Sharma S, Sharma MC, Gupta DK, et al. Angiogenic patterns and their quantitation in high grade astrocytic tumors. J Neurooncol. 2006;79:19–30.CrossRefPubMedGoogle Scholar
  46. 46.
    Takahashi T. Microcirculation in fractal branching networks. Tokyo: Springer; 2014.CrossRefGoogle Scholar
  47. 47.
    Vakoc BJ, Lanning RM, Tyrrell JA, et al. Three-dimensional microscopy of the tumor microenvironment in vivo using optical frequency domain imaging. Nat Med. 2009;15:1219–23.CrossRefPubMedPubMedCentralGoogle Scholar
  48. 48.
    van den Bent MJ. Interobserver variation of the histopathological diagnosis in clinical trials on glioma: a clinician’s perspective. Acta Neuropathol. 2010;120:297–304.CrossRefPubMedPubMedCentralGoogle Scholar
  49. 49.
    Vico PG, Kyriacos S, Heymans O, et al. Dynamic study of the extraembryonic vascular network of the chick embryo by fractal analysis. J Theor Biol. 1998;195:525–32.CrossRefPubMedGoogle Scholar
  50. 50.
    Zamir M. On fractal properties of arterial trees. J Theor Biol. 1999;197:517–26.CrossRefPubMedGoogle Scholar
  51. 51.
    Zamir M. Fractal dimensions and multifractility in vascular branching. J Theor Biol. 2001;212:183–90.CrossRefPubMedGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Neurosurgery Unit, Faculty of Medicine and Health SciencesMacquarie UniversitySydneyAustralia
  2. 2.Garvan Institute of Medical ResearchSydneyAustralia
  3. 3.Medical University of ViennaViennaAustria
  4. 4.University of TorontoTorontoCanada
  5. 5.Institute of BioengineeringEcole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  6. 6.King Abdullah II School for ITUniversity of JordanAmmanJordan

Personalised recommendations