Computational fractal-based analysis of brain tumor microvascular networks

Antonio Di Ieva, Omar S. Al-Kadi

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

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.
LanguageEnglish
Title of host publicationThe Fractal geometry of the brain
EditorsAntonio Di Ieva
Place of PublicationNew York
PublisherSpringer, Springer Nature
Pages393-411
Number of pages19
ISBN (Print)9781493939954
DOIs
Publication statusPublished - 2016

Publication series

NameSpringer Series in Computational Neuroscience
PublisherSpringer

Fingerprint

Fractals
Microvessels
Brain Neoplasms
Neoplasms
Dermatoglyphics
Tumor Biomarkers
Brain

Keywords

  • angioarchitecture
  • brain tumor
  • fractal dimension
  • fractal analysis
  • glioblastoma multiforme
  • microvascularity

Cite this

Di Ieva, A., & Al-Kadi, O. S. (2016). Computational fractal-based analysis of brain tumor microvascular networks. In A. Di Ieva (Ed.), The Fractal geometry of the brain (pp. 393-411). (Springer Series in Computational Neuroscience). New York: Springer, Springer Nature. https://doi.org/10.1007/978-1-4939-3995-4_24
Di Ieva, Antonio ; Al-Kadi, Omar S. / Computational fractal-based analysis of brain tumor microvascular networks. The Fractal geometry of the brain. editor / Antonio Di Ieva. New York : Springer, Springer Nature, 2016. pp. 393-411 (Springer Series in Computational Neuroscience).
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Di Ieva, A & Al-Kadi, OS 2016, Computational fractal-based analysis of brain tumor microvascular networks. in A Di Ieva (ed.), The Fractal geometry of the brain. Springer Series in Computational Neuroscience, Springer, Springer Nature, New York, pp. 393-411. https://doi.org/10.1007/978-1-4939-3995-4_24

Computational fractal-based analysis of brain tumor microvascular networks. / Di Ieva, Antonio; Al-Kadi, Omar S.

The Fractal geometry of the brain. ed. / Antonio Di Ieva. New York : Springer, Springer Nature, 2016. p. 393-411 (Springer Series in Computational Neuroscience).

Research output: Chapter in Book/Report/Conference proceedingChapterResearchpeer-review

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Di Ieva A, Al-Kadi OS. Computational fractal-based analysis of brain tumor microvascular networks. In Di Ieva A, editor, The Fractal geometry of the brain. New York: Springer, Springer Nature. 2016. p. 393-411. (Springer Series in Computational Neuroscience). https://doi.org/10.1007/978-1-4939-3995-4_24