Machine learning and glioma imaging biomarkers.

Journal: Clinical radiology
Published Date: Jul 29, 2019

Abstract

AIM: To review how machine learning (ML) is applied to imaging biomarkers in neuro-oncology, in particular for diagnosis, prognosis, and treatment response monitoring.

Authors

  • T C Booth
    School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, UK; Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London SE5 9RS, UK. Electronic address: thomascalvertbooth@gmail.com.
  • M Williams
    Department of Neuro-oncology, Imperial College Healthcare NHS Trust, Fulham Palace Rd, London W6 8RF, UK.
  • A Luis
    School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, UK; Department of Radiology, St George's University Hospitals NHS Foundation Trust, Blackshaw Road, London SW17 0QT, UK.
  • J Cardoso
    School of Biomedical Engineering & Imaging Sciences, King's College London, St Thomas' Hospital, London SE1 7EH, UK.
  • K Ashkan
    Department of Neurosurgery, King's College Hospital NHS Foundation Trust, London SE5 9RS, UK.
  • H Shuaib
    Department of Medical Physics, Guy's & St. Thomas' NHS Foundation Trust, London SE1 7EH, UK; Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, SE5 8AF, UK.

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