Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists.

Journal: European radiology
Published Date: Jun 11, 2019

Abstract

OBJECTIVE: The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists.

Authors

  • Michela Antonelli
    Centre for Medical Image Computing, University College London, London, UK.
  • Edward W Johnston
    Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
  • Nikolaos Dikaios
    Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
  • King K Cheung
    Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
  • Harbir S Sidhu
    Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
  • Mrishta B Appayya
    Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
  • Francesco Giganti
    Department of Radiology, University College London Hospital, London, UK.
  • Lucy A M Simmons
    Division of Surgery and Interventional Science, University College London, London, UK.
  • Alex Freeman
    Department of Pathology, University College London Hospital, London, UK.
  • Clare Allen
    Department of Radiology, University College London Hospital, London, UK.
  • Hashim U Ahmed
    Division of Surgery and Interventional Science, University College London, London, UK.
  • David Atkinson
    Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
  • Sébastien Ourselin
    Wellcome / EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, UK.
  • Shonit Punwani
    Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK. shonit.punwani@gmail.com.

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