  In recent years, the diagnosis of gliomas has become increasingly complex.
Analysis of glioma histopathology images using artificial intelligence (AI)
offers new opportunities to support diagnosis and outcome prediction. To give
an overview of the current state of research, this review examines 83 publicly
available research studies that have proposed AI-based methods for whole-slide
histopathology images of human gliomas, covering the diagnostic tasks of
subtyping (23/83), grading (27/83), molecular marker prediction (20/83), and
survival prediction (29/83). All studies were reviewed with regard to
methodological aspects as well as clinical applicability. It was found that the
focus of current research is the assessment of hematoxylin and eosin-stained
tissue sections of adult-type diffuse gliomas. The majority of studies (52/83)
are based on the publicly available glioblastoma and low-grade glioma datasets
from The Cancer Genome Atlas (TCGA) and only a few studies employed other
datasets in isolation (16/83) or in addition to the TCGA datasets (15/83).
Current approaches mostly rely on convolutional neural networks (63/83) for
analyzing tissue at 20x magnification (35/83). A new field of research is the
integration of clinical data, omics data, or magnetic resonance imaging
(29/83). So far, AI-based methods have achieved promising results, but are not
yet used in real clinical settings. Future work should focus on the independent
validation of methods on larger, multi-site datasets with high-quality and
up-to-date clinical and molecular pathology annotations to demonstrate routine
applicability.
