  Primary brain tumors including gliomas continue to pose significant
management challenges to clinicians. While the presentation, the pathology, and
the clinical course of these lesions are variable, the initial investigations
are usually similar. Patients who are suspected to have a brain tumor will be
assessed with computed tomography (CT) and magnetic resonance imaging (MRI).
The imaging findings are used by neurosurgeons to determine the feasibility of
surgical resection and plan such an undertaking. Imaging studies are also an
indispensable tool in tracking tumor progression or its response to treatment.
As these imaging studies are non-invasive, relatively cheap and accessible to
patients, there have been many efforts over the past two decades to increase
the amount of clinically-relevant information that can be extracted from brain
imaging. Most recently, artificial intelligence (AI) techniques have been
employed to segment and characterize brain tumors, as well as to detect
progression or treatment-response. However, the clinical utility of such
endeavours remains limited due to challenges in data collection and annotation,
model training, and the reliability of AI-generated information.
  We provide a review of recent advances in addressing the above challenges.
First, to overcome the challenge of data paucity, different image imputation
and synthesis techniques along with annotation collection efforts are
summarized. Next, various training strategies are presented to meet multiple
desiderata, such as model performance, generalization ability, data privacy
protection, and learning with sparse annotations. Finally, standardized
performance evaluation and model interpretability methods have been reviewed.
We believe that these technical approaches will facilitate the development of a
fully-functional AI tool in the clinical care of patients with gliomas.
