In recent years, the diagnosis of gliomas has become increasingly complex.
Histological assessment of glioma tissue using modern machine learning
techniques offers new opportunities to support diagnosis and outcome
prediction. To give an overview of the current state of research, this review
examines 70 publicly available research studies on machine learning-based
analysis of stained human glioma tissue sections, covering the diagnostic tasks
of subtyping (16/70), grading (23/70), molecular marker prediction (13/70), and
survival prediction (27/70). 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 (49/70)
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 (10/70) or in addition to the TCGA datasets (11/70).
Current approaches mostly rely on convolutional neural networks (53/70) for
analyzing tissue at 20x magnification (30/70). A new field of research is the
integration of clinical data, omics data, or magnetic resonance imaging
(27/70). So far, machine learning-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.

近年来，诊断胶质瘤变得越来越复杂。通过现代机器学习技术对胶质瘤组织进行组织学评估为支持诊断和预测结果提供了新机会。本综述通过对 70 项公开的基于机器学习的染色人类胶质瘤组织切片分析研究进行概述，包括亚型划分（16/70）、分级（23/70）、分子标记预测（13/70）和生存预测（27/70）。当前研究的重点是对成人型弥漫性胶质瘤的苏木精和伊红染色组织切片的评估。大多数研究（49/70）基于公开数据集，包括癌症基因组图谱（TCGA）的胶质母细胞瘤和低级别胶质瘤数据集，只有少数的研究孤立地使用其他数据集（10/70）或者与 TCGA 数据集同时使用（11/70）。目前的方法主要依赖于卷积神经网络（53/70）对 20 倍放大率的组织进行分析（30/70）。一个新的研究领域是整合临床数据、组学数据或磁共振成像（27/70）。到目前为止，基于机器学习的方法已取得了有希望的结果，但尚未在真实临床环境中使用。未来的工作应集中在使用大型、多中心、具有高质量和最新的临床和分子病理学注释的独立验证方法上，以证明其日常应用性。