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Computational Fractal-Based Analysis of Brain Tumor Microvascular Networks

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Book cover The Fractal Geometry of the Brain

Part of the book series: Springer Series in Computational Neuroscience ((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.

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Notes

  1. 1.

    At the time of the chapter writing, the WHO classification system for brain tumors published in 2016 was not available yet.

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Author information

Authors and Affiliations

  1. Neurosurgery Unit, Faculty of Medicine and Health Sciences, Macquarie University, Sydney, NSW, Australia

    Antonio Di Ieva MD, PhD

  2. Garvan Institute of Medical Research, Sydney, NSW, Australia

    Antonio Di Ieva MD, PhD

  3. Medical University of Vienna, Vienna, Austria

    Antonio Di Ieva MD, PhD

  4. University of Toronto, Toronto, ON, Canada

    Antonio Di Ieva MD, PhD

  5. Institute of Bioengineering, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

    Omar S. Al-Kadi

  6. King Abdullah II School for IT, University of Jordan, Amman, Jordan

    Omar S. Al-Kadi

Authors
  1. Antonio Di Ieva MD, PhD
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  2. Omar S. Al-Kadi
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Corresponding author

Correspondence to Antonio Di Ieva MD, PhD .

Editor information

Editors and Affiliations

  1. Department of Surgery Division of Neurosurgery, University of Toronto St. Michael's Hospital, Toronto, Ontario, Canada

    Antonio Di Ieva

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Di Ieva, A., Al-Kadi, O.S. (2016). Computational Fractal-Based Analysis of Brain Tumor Microvascular Networks. In: Di Ieva, A. (eds) The Fractal Geometry of the Brain. Springer Series in Computational Neuroscience. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-3995-4_24

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