The central role of MRI in neuro-oncology is undisputed. The technique is used, both in clinical practice and in clinical trials, to diagnose and monitor disease activity, support treatment decision-making, guide the use of focused treatments and determine response to treatment. Despite recent substantial advances in imaging technology and image analysis techniques, clinical MRI is still primarily used for the qualitative subjective interpretation of macrostructural features, as opposed to quantitative analyses that take into consideration multiple pathophysiological features. However, the field of quantitative imaging and imaging biomarker development is maturing. The European Imaging Biomarkers Alliance (EIBALL) and Quantitative Imaging Biomarkers Alliance (QIBA) are setting standards for biomarker development, validation and implementation, as well as promoting the use of quantitative imaging and imaging biomarkers by demonstrating their clinical value. In parallel, advanced imaging techniques are reaching the clinical arena, providing quantitative, commonly physiological imaging parameters that are driving the discovery, validation and implementation of quantitative imaging and imaging biomarkers in the clinical routine. Additionally, computational analysis techniques are increasingly being used in the research setting to convert medical images into objective high-dimensional data and define radiomic signatures of disease states. Here, I review the definition and current state of MRI biomarkers in neuro-oncology, and discuss the clinical potential of quantitative image analysis techniques.

MRI 在神经肿瘤学中的核心作用是无可争议的。该技术在临床实践和临床试验中用于诊断和监测疾病活动、支持治疗决策、指导重点治疗的使用并确定对治疗的反应。尽管最近在成像技术和图像分析技术方面取得了重大进展，但临床 MRI 仍然主要用于宏观结构特征的定性主观解释，而不是考虑多种病理生理特征的定量分析。然而，定量成像和成像生物标志物开发领域正在走向成熟。欧洲成像生物标志物联盟 (EIBALL) 和定量成像生物标志物联盟 (QIBA) 正在制定生物标志物开发、验证和实施的标准，以及通过展示其临床价值来促进定量成像和成像生物标志物的使用。与此同时，先进的成像技术正在进入临床领域，提供定量的、常见的生理成像参数，这些参数正在推动临床常规中定量成像和成像生物标志物的发现、验证和实施。此外，计算分析技术越来越多地用于研究环境，以将医学图像转换为客观的高维数据并定义疾病状态的放射学特征。在这里，我回顾了神经肿瘤学中 MRI 生物标志物的定义和现状，并讨论了定量图像分析技术的临床潜力。