  Aim: To review how machine learning (ML) is applied to imaging biomarkers in
neuro-oncology, in particular for diagnosis, prognosis, and treatment response
monitoring.
  Materials and Methods: The PubMed and MEDLINE databases were searched for
articles published before September 2018 using relevant search terms. The
search strategy focused on articles applying ML to high-grade glioma biomarkers
for treatment response monitoring, prognosis, and prediction.
  Results: Magnetic resonance imaging (MRI) is typically used throughout the
patient pathway because routine structural imaging provides detailed anatomical
and pathological information and advanced techniques provide additional
physiological detail. Using carefully chosen image features, ML is frequently
used to allow accurate classification in a variety of scenarios. Rather than
being chosen by human selection, ML also enables image features to be
identified by an algorithm. Much research is applied to determining molecular
profiles, histological tumour grade, and prognosis using MRI images acquired at
the time that patients first present with a brain tumour. Differentiating a
treatment response from a post-treatment-related effect using imaging is
clinically important and also an area of active study (described here in one of
two Special Issue publications dedicated to the application of ML in glioma
imaging).
  Conclusion: Although pioneering, most of the evidence is of a low level,
having been obtained retrospectively and in single centres. Studies applying ML
to build neuro-oncology monitoring biomarker models have yet to show an overall
advantage over those using traditional statistical methods. Development and
validation of ML models applied to neuro-oncology require large, well-annotated
datasets, and therefore multidisciplinary and multi-centre collaborations are
necessary.
