Although imaging of gliomas has evolved tremendously over the last decades, published techniques and protocols are not always implemented into clinical practice. Furthermore, most of the published literature focuses on specific timepoints in glioma management. This article reviews the current literature on conventional and advanced imaging techniques and chronologically outlines their practical relevance for the clinical management of gliomas throughout the cycle of care. Relevant articles were located through the Pubmed/Medline database and included in this review. Interpretation of conventional and advanced imaging techniques is crucial along the entire process of glioma care, from diagnosis to follow-up. In addition to the described currently existing techniques, we expect deep learning or machine learning approaches to assist each step of glioma management through tumor segmentation, radiogenomics, prognostication, and characterization of pseudoprogression. Thorough knowledge of the specific performance, possibilities, and limitations of each imaging modality is key for their adequate use in glioma management.

尽管神经胶质瘤的成像在过去几十年中取得了巨大的发展，但已发表的技术和协议并不总是在临床实践中得到实施。此外，大多数已发表的文献都侧重于胶质瘤管理的特定时间点。本文回顾了有关传统和先进成像技术的当前文献，并按时间顺序概述了它们在整个护理周期中与胶质瘤临床管理的实际相关性。相关文章通过 Pubmed/Medline 数据库找到并包含在本综述中。在胶质瘤治疗的整个过程中，从诊断到随访，对传统和先进成像技术的解释至关重要。除了描述的现有技术之外，我们期望深度学习或机器学习方法通​​过肿瘤分割、放射基因组学、预测和假性进展的表征来协助胶质瘤管理的每一步。对每种成像方式的具体性能、可能性和局限性的全面了解是它们在神经胶质瘤管理中充分使用的关键。