Machine Learning in Neurooncology Imaging: From Study Request to Diagnosis and Treatment.

Journal: AJR. American journal of roentgenology
Published Date: Nov 7, 2018

Abstract

OBJECTIVE: Machine learning has potential to play a key role across a variety of medical imaging applications. This review seeks to elucidate the ways in which machine learning can aid and enhance diagnosis, treatment, and follow-up in neurooncology.

Authors

  • Javier E Villanueva-Meyer
    1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, L-352, San Francisco, CA 94143.
  • Peter Chang
    Department of Urology, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Janine M Lupo
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; UCSF/UC Berkeley Graduate Group in Bioengineering, USA. Electronic address: janine.lupo@ucsf.edu.
  • Christopher P Hess
    Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA; Department of Neurology, University of California San Francisco, San Francisco, CA, USA.
  • Adam E Flanders
  • Marc Kohli
    1 Department of Radiology and Biomedical Imaging, University of California, San Francisco, 505 Parnassus Ave, M-391, San Francisco, CA 94143.

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