Department of Neurology, Faculty of Medicine, University Hospital Cologne, University of Cologne, Cologne, Germany.
Institute of Neuroscience and Medicine (INM-3, -4), Research Center Juelich, Juelich, Germany.
Brain Pathol. 2022 Mar;32(2):e13015. doi: 10.1111/bpa.13015.
Anatomical cross-sectional imaging methods such as contrast-enhanced MRI and CT are the standard for the delineation, treatment planning, and follow-up of patients with meningioma. Besides, advanced neuroimaging is increasingly used to non-invasively provide detailed insights into the molecular and metabolic features of meningiomas. These techniques are usually based on MRI, e.g., perfusion-weighted imaging, diffusion-weighted imaging, MR spectroscopy, and positron emission tomography. Furthermore, artificial intelligence methods such as radiomics offer the potential to extract quantitative imaging features from routinely acquired anatomical MRI and CT scans and advanced imaging techniques. This allows the linking of imaging phenotypes to meningioma characteristics, e.g., the molecular-genetic profile. Here, we review several diagnostic applications and future directions of these advanced neuroimaging techniques, including radiomics in preclinical models and patients with meningioma.
解剖性横断面成像方法,如增强 MRI 和 CT,是脑膜瘤患者的划定、治疗计划和随访的标准。此外,先进的神经影像学越来越多地用于非侵入性地提供对脑膜瘤分子和代谢特征的详细了解。这些技术通常基于 MRI,例如灌注加权成像、扩散加权成像、磁共振波谱和正电子发射断层扫描。此外,人工智能方法(如放射组学)有可能从常规获取的解剖 MRI 和 CT 扫描以及先进的成像技术中提取定量成像特征。这使得可以将成像表型与脑膜瘤特征(例如分子遗传特征)联系起来。在这里,我们回顾了这些先进的神经影像学技术的一些诊断应用和未来方向,包括脑膜瘤的临床前模型和患者的放射组学。