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 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.

中文翻译：

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高级别胶质瘤肿瘤内异质性的成像。

高度神经胶质瘤（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，从而混淆了临床诊断和治疗。尽管常规的对比增强MRI缺乏解决这种异质性的能力，但先进的MRI技术提供了一系列生理和生物物理图像特征，以提高成像诊断的特异性。已发表的研究表明，整合这些先进技术如何帮助更好地定义组织学上不同的手术和放射治疗计划靶标，并帮助评估标准辅助治疗后肿瘤复发的区域异质性和反应评估。纹理分析和机器学习（ML）算法的应用也使无线电基因组学领域兴起，它可以在空间上解决单个GBM肿瘤中共存的区域和遗传上不同的亚群。这篇综述着重于神经肿瘤影像学的最新进展及其在肿瘤内异质性评估中的临床应用。