Department of Radiology, Mayo Clinic Arizona, 5777 E Mayo Blvd, Phoenix, AZ, 85054, USA.
Mathematical NeuroOncology Lab, Precision Neurotherapeutics Innovation Program, Mayo Clinic, 5777 East Mayo Blvd, Support, Services Building Suite 2-700, Phoenix, AZ, 85054, USA.
Cancer Lett. 2020 May 1;477:97-106. doi: 10.1016/j.canlet.2020.02.025. Epub 2020 Feb 27.
High-grade glioma (HGG), and particularly Glioblastoma (GBM), can exhibit pronounced intratumoral heterogeneity that confounds clinical diagnosis and management. While conventional contrast-enhanced MRI lacks the capability to resolve this heterogeneity, advanced MRI techniques and PET imaging offer a spectrum of physiologic and biophysical image features to improve the specificity of imaging diagnoses. Published studies have shown how integrating these advanced techniques can help better define histologically distinct targets for surgical and radiation treatment planning, and help evaluate the regional heterogeneity of tumor recurrence and response assessment following standard adjuvant therapy. Application of texture analysis and machine learning (ML) algorithms has also enabled the emerging field of radiogenomics, which can spatially resolve the regional and genetically distinct subpopulations that coexist within a single GBM tumor. This review focuses on the latest advances in neuro-oncologic imaging and their clinical applications for the assessment of intratumoral heterogeneity.
高级别胶质瘤（HGG），尤其是胶质母细胞瘤（GBM），可能表现出明显的肿瘤内异质性，这使得临床诊断和管理变得复杂。虽然常规的对比增强 MRI 缺乏解析这种异质性的能力，但先进的 MRI 技术和 PET 成像提供了一系列生理和生物物理的图像特征，以提高成像诊断的特异性。已发表的研究表明，如何整合这些先进技术可以帮助更好地定义手术和放射治疗计划中具有组织学差异的目标，并有助于评估标准辅助治疗后肿瘤复发和反应评估的区域异质性。纹理分析和机器学习（ML）算法的应用也使得放射基因组学这一新兴领域成为可能，该领域可以在单个 GBM 肿瘤内共存的具有空间分辨率的区域和遗传上不同的亚群。这篇综述重点介绍神经肿瘤学成像的最新进展及其在评估肿瘤内异质性方面的临床应用。
