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Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning

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Machine Learning in Clinical Neuroscience

Part of the book series: Acta Neurochirurgica Supplement ((NEUROCHIRURGICA,volume 134))

Abstract

The heterogeneity of brain tumours at the molecular, metabolic and structural levels poses significant challenge for accurate tissue characterisation. Artificial intelligence and radiomics have emerged as valuable tools to analyse quantitative features extracted from medical images which capture the complex microenvironment of brain tumours. In particular, a number of computational tools including machine learning algorithms have been proposed for image preprocessing, tumour segmentation, feature extraction, classification, and prognostic stratifications as well. In this chapter, we explore the fundamentals of multiparametric brain tumour characterisation, as an understanding of the strengths, limitations and applications of these tools allows clinicians to better develop and evaluate models with improved diagnostic and prognostic value in brain tumour patients.

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Acknowledgements

Prof. Antonio Di Ieva acknowledges the Royal Australasian College of Surgeons (RACS) for the 2019 John Mitchell Crouch Fellowship, which allowed the foundation of the Computational NeuroSurgery (CNS) Lab at Macquarie University. Moreover, he is funded by the Australian National Health and Medical Research Council (NHMRC) and by the Australian Research Council (ARC).

Dr. Sidong Liu acknowledges the support of an Australian National Health and Medical Research Council grant (NHMRC Early Career Fellowship).

Conflict of Interest

The authors declare that they have no conflict of interest.

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Authors and Affiliations

  1. Computational NeuroSurgery (CNS) Lab, Macquarie Medical School, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, NSW, Australia

    Anne Jian, Kevin Jang, Carlo Russo, Sidong Liu & Antonio Di Ieva

  2. Royal Melbourne Hospital, Melbourne, VIC, Australia

    Anne Jian

  3. Faculty of Medicine and Health, The University of Sydney, Sydney, NSW, Australia

    Kevin Jang

  4. Centre for Health Informatics, Macquarie University, Sydney, NSW, Australia

    Sidong Liu

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Correspondence to Antonio Di Ieva .

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Editors and Affiliations

  1. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Victor E. Staartjes

  2. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Luca Regli

  3. Machine Intelligence in Clinical Neuroscience (MICN) Laboratory, Department of Neurosurgery, Clinical Neuroscience Center, University Hospital Zurich, University of Zurich, Zurich, Switzerland

    Carlo Serra

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Jian, A., Jang, K., Russo, C., Liu, S., Di Ieva, A. (2022). Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning. In: Staartjes, V.E., Regli, L., Serra, C. (eds) Machine Learning in Clinical Neuroscience. Acta Neurochirurgica Supplement, vol 134. Springer, Cham. https://doi.org/10.1007/978-3-030-85292-4_22

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