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Advanced computational and statistical multiparametric analysis of Susceptibility-Weighted Imaging to characterize gliomas and brain metastases

View ORCID ProfileAntonio Di Ieva, View ORCID ProfileCarlo Russo, View ORCID ProfilePierre-Jean Le Reste, View ORCID ProfileJohn S. Magnussen, View ORCID ProfileGillian Heller
doi: https://doi.org/10.1101/2020.04.24.060830
Antonio Di Ieva
1Computational NeuroSurgery (CNS) Lab, Macquarie University, Sydney, NSW, Australia
2Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
3Macquarie Neurosurgery, Sydney, Australia
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  • For correspondence: antonio.diieva@mq.edu.au
Carlo Russo
1Computational NeuroSurgery (CNS) Lab, Macquarie University, Sydney, NSW, Australia
2Department of Clinical Medicine, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia
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Pierre-Jean Le Reste
4Department of Neurosurgery, University Hospital, Rennes, France
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John S. Magnussen
1Computational NeuroSurgery (CNS) Lab, Macquarie University, Sydney, NSW, Australia
5Macquarie Medical Imaging, Macquarie University, Sydney, New South Wales, Australia
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Gillian Heller
6Department of Statistics, Macquarie University, Sydney, NSW, Australia
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Abstract

Susceptibility-weighted imaging (SWI) is a technique useful for evaluation of the internal structures of brain tumors, including microvasculature and microbleeds. Intratumoral patterns of magnetic susceptibility can be quantified by means of fractal analysis. Here, we propose a radiomics methodological pipeline to merge advanced fractal-based computational modelling with statistical analysis to objectively characterize the fingerprint of gliomas and brain metastases. Forty-seven patients with glioma (grades II-IV, according to the WHO 2016 classification system) and fourteen with brain metastases underwent 3 Tesla MRI using a SWI protocol. All images underwent computational analysis aimed to quantify three Euclidean parameters (related to tumor and SWI volume) and five fractal-based parameters (related to the pixel distribution and geometrical complexity of the SWI patterns). Principal components analysis, linear and quadratic discriminant analysis, K-nearest neighbor and support vector machine methods were used to discriminate between tumor types. The combination of parameters offered an objective evaluation of the SWI pattern in gliomas and brain metastases. The model accurately predicted 88% of glioblastoma, according to the quantification of intratumoral SWI features, failing to discriminate the other types. SWI is not normally used to classify brain tumors, however fractal-based multi-parametric computational analysis can be used to characterize intratumoral SWI patterns to objectively quantify tumors-related features. Specific parameters still have to be identified to provide completely automatic computerized differential diagnosis.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • The preliminary results of this study were presented at the 15th Annual World Congress of the Society for Brain Mapping & Therapeutics (SBMT), 13 April 2018, Los Angeles, California, USA

  • Abbreviations
    FD
    Fractal Dimension
    WHO
    World Health Organization
    GBM
    Glioblastoma
    ROI
    Region of interest
    SWI
    Susceptibility-weighted imaging
  • Copyright 
    The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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    Posted April 25, 2020.
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    Advanced computational and statistical multiparametric analysis of Susceptibility-Weighted Imaging to characterize gliomas and brain metastases
    Antonio Di Ieva, Carlo Russo, Pierre-Jean Le Reste, John S. Magnussen, Gillian Heller
    bioRxiv 2020.04.24.060830; doi: https://doi.org/10.1101/2020.04.24.060830
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    Advanced computational and statistical multiparametric analysis of Susceptibility-Weighted Imaging to characterize gliomas and brain metastases
    Antonio Di Ieva, Carlo Russo, Pierre-Jean Le Reste, John S. Magnussen, Gillian Heller
    bioRxiv 2020.04.24.060830; doi: https://doi.org/10.1101/2020.04.24.060830

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