Department of Neurosurgery, University Hospital of Liège, Liège, Belgium.
GIGA-CRC In-vivo Imaging Center, ULiege, Liège, Belgium.
Neurosurg Rev. 2021 Oct;44(5):2493-2509. doi: 10.1007/s10143-020-01448-3. Epub 2021 Jan 7.
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.
尽管过去几十年来神经胶质瘤的影像学已经有了巨大的发展，但发表的技术和方案并不总是在临床实践中得到实施。此外，大多数发表的文献都集中在神经胶质瘤管理的特定时间点上。本文综述了常规和先进影像学技术的最新文献，并按时间顺序概述了它们在整个治疗周期中对神经胶质瘤临床管理的实际意义。通过 Pubmed/Medline 数据库找到了相关文章，并纳入了本综述。在神经胶质瘤护理的整个过程中，从诊断到随访，对常规和先进影像学技术的解读至关重要。除了描述的现有技术外，我们还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、预后和假性进展的特征来协助神经胶质瘤管理的每一步。深入了解每种成像方式的特定性能、可能性和局限性是在神经胶质瘤管理中充分利用它们的关键。
