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Foundations of Multiparametric Brain Tumour Imaging Characterisation Using Machine Learning.

Anne Jian1,2, Kevin Jang1,3, Carlo Russo1

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

Acta Neurochirurgica. Supplement
|December 4, 2021
PubMed
Summary
This summary is machine-generated.

Artificial intelligence and radiomics offer powerful tools for analyzing brain tumor characteristics. These computational methods aid in improving tumor classification and patient prognosis.

Keywords:
Brain tumourMRIMachine learningMultiparametric characterisationRadiomics

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Area of Science:

  • Neuro-oncology
  • Medical Imaging Analysis
  • Computational Pathology

Background:

  • Brain tumor heterogeneity presents challenges in accurate tissue characterization.
  • Quantitative imaging analysis is crucial for understanding tumor microenvironments.
  • Artificial intelligence (AI) and radiomics are emerging as key technologies in this field.

Purpose of the Study:

  • To explore the fundamentals of multiparametric brain tumor characterization.
  • To understand the strengths, limitations, and applications of AI and radiomics in neuro-oncology.
  • To guide the development and evaluation of models for improved diagnostic and prognostic value.

Main Methods:

  • Utilizing artificial intelligence and radiomics for quantitative feature extraction from medical images.
  • Employing machine learning algorithms for image preprocessing and tumor segmentation.
  • Applying computational tools for feature extraction, classification, and prognostic stratification.

Main Results:

  • AI and radiomics enable detailed analysis of complex brain tumor microenvironments.
  • Computational tools facilitate various stages of image analysis, from preprocessing to prognosis.
  • Understanding these tools is essential for enhancing diagnostic accuracy and prognostic capabilities.

Conclusions:

  • AI and radiomics are invaluable for multiparametric brain tumor characterization.
  • Effective application of these technologies can significantly improve patient outcomes.
  • Further development and evaluation of AI-driven models are critical in neuro-oncology.