Radiomics is a rapidly evolving field of research concerned with the extraction of quantitative metrics—the so-called radiomic features—within medical images. Radiomic features capture tissue and lesion characteristics such as heterogeneity and shape and may, alone or in combination with demographic, histologic, genomic, or proteomic data, be used for clinical problem solving. The goal of this continuing education article is to provide an introduction to the field, covering the basic radiomics workflow: feature calculation and selection, dimensionality reduction, and data processing. Potential clinical applications in nuclear medicine that include PET radiomics-based prediction of treatment response and survival will be discussed. Current limitations of radiomics, such as sensitivity to acquisition parameter variations, and common pitfalls will also be covered.

放射线学是一个快速发展的研究领域，涉及在医学图像中提取定量指标（所谓的放射线特征）。放射学特征捕获组织和病变特征（例如异质性和形状），并且可以单独使用，也可以结合人口统计学，组织学，基因组或蛋白质组学数据，用于临床问题解决。这篇继续教育文章的目的是提供对该领域的介绍，涵盖基本的放射学工作流程：特征计算和选择，降维和数据处理。将讨论核医学的潜在临床应用，包括基于PET放射学的治疗反应和生存预测。放射线学的当前限制，例如对采集参数变化的敏感性，