Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York
Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, Vienna, Austria.
J Nucl Med. 2020 Apr;61(4):488-495. doi: 10.2967/jnumed.118.222893. Epub 2020 Feb 14.
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.
放射组学是一个快速发展的研究领域，专注于从医学图像中提取定量指标，即所谓的放射组学特征。放射组学特征可捕获组织和病变特征，如异质性和形状，并且可以单独或与人口统计学、组织学、基因组学或蛋白质组学数据结合使用，以解决临床问题。本文旨在提供该领域的简介，涵盖基本的放射组学工作流程：特征计算和选择、降维和数据处理。还将讨论核医学中的潜在临床应用，包括基于 PET 放射组学预测治疗反应和生存。还将涵盖放射组学的当前局限性，例如对采集参数变化的敏感性以及常见的陷阱。
