Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers that characterize intra-tumoral dynamics throughout the course of treatment. This is particularly difficult in gliomas, where heterogeneity has been well established at a molecular level as well as visually in conventional imaging. Thus, acquiring clinically useful features remains difficult due to temporal variations in tumor dynamics. Identifying surrogate biomarkers through radiomics may provide a non-invasive means of characterizing biologic activities of gliomas. We present an extensive literature review of radiomics-based analysis, with a particular focus on computational modeling, machine learning, and fractal-based analysis in improving differential diagnosis and predicting clinical outcomes. Novel strategies in extracting quantitative features, segmentation methods, and their clinical applications are producing promising results. Moreover, we provide a detailed summary of the morphometric parameters that have so far been proposed as a means of quantifying imaging characteristics of gliomas. Newly emerging radiomic techniques via machine learning and fractal-based analyses holds considerable potential for improving diagnostic and prognostic accuracy of gliomas.

Key points

• Radiomic features can be mined through computational analysis to produce quantitative imaging biomarkers that characterize intra-tumoral dynamics throughout the course of treatment.

• Surrogate image biomarkers identified through radiomics could enable a non-invasive means of characterizing biologic activities of gliomas.

• With novel analytic algorithms, quantification of morphological or sub-regional tumor features to predict survival outcomes is producing promising results.

• Quantifying intra-tumoral heterogeneity may improve grading and molecular sub-classifications of gliomas.

• Computational fractal-based analysis of gliomas allows geometrical evaluation of tumor irregularities and complexity, leading to novel techniques for tumor segmentation, grading, and therapeutic monitoring.

放射医学是一个新兴领域，涉及从医学图像中提取和量化特征。这些数据可以通过计算分析和模型来挖掘，以鉴定出在整个治疗过程中表征肿瘤内动态的预测性图像生物标志物。这在神经胶质瘤中尤其困难，在传统的成像中，在神经胶质瘤中在分子水平和视觉上均已很好地确定了异质性。因此，由于肿瘤动力学的时间变化，获得临床有用的特征仍然困难。通过放射组学鉴定替代生物标志物可能提供表征神经胶质瘤生物活性的非侵入性手段。我们将对基于放射组学的分析进行广泛的文献综述，重点是计算建模，机器学习，基于分形的分析可改善鉴别诊断和预测临床结果。提取定量特征，分割方法及其临床应用的新策略正在产生可喜的结果。此外，我们提供了形态计量学参数的详细摘要，到目前为止，已提出形态学参数作为量化神经胶质瘤成像特征的手段。通过机器学习和基于分形分析的新兴放射技术在提高神经胶质瘤的诊断和预后准确性方面具有巨大潜力。我们提供了形态计量学参数的详细摘要，迄今为止已提出形态学参数，作为量化神经胶质瘤成像特征的一种手段。通过机器学习和基于分形分析的新兴放射技术在提高神经胶质瘤的诊断和预后准确性方面具有巨大潜力。我们提供了形态计量学参数的详细摘要，迄今为止已提出形态学参数，作为量化神经胶质瘤成像特征的一种手段。通过机器学习和基于分形分析的新兴放射技术在提高神经胶质瘤的诊断和预后准确性方面具有巨大潜力。

关键点

•放射学特征可通过计算分析进行挖掘，以产生定量成像生物标记物，这些标记物表征整个治疗过程中的肿瘤内动力学。

•通过放射组学鉴定的替代图像生物标记物可以实现表征神经胶质瘤生物活性的非侵入性手段。

•利用新颖的分析算法，对形态或亚区域肿瘤特征进行量化以预测生存结果正在产生可喜的结果。

•量化肿瘤内异质性可以改善神经胶质瘤的分级和分子亚分类。

•基于分形的神经胶质瘤分析可以对肿瘤的不规则性和复杂性进行几何评估，从而带来了用于肿瘤分割，分级和治疗监测的新技术。