Foundations of multiparametric brain tumor imaging characterization using machine learning

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

*Corresponding author for this work

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

6 Citations (Scopus)


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.

Original languageEnglish
Title of host publicationMachine learning in clinical neuroscience
Subtitle of host publicationfoundations and applications
EditorsVictor E. Staartjes, Luca Regli, Carlo Serra
Place of PublicationSwitzerland
PublisherSpringer, Springer Nature
Number of pages11
ISBN (Electronic)9783030852924
ISBN (Print)9783030852917
Publication statusPublished - 2022

Publication series

NameActa Neurochirurgica Supplement
ISSN (Print)0065-1419
ISSN (Electronic)2197-8395


  • Brain tumour
  • Machine learning
  • MRI
  • Multiparametric characterisation
  • Radiomics


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