Radiomics is an emerging area in quantitative image analysis that aims to relate large‐scale extracted imaging information to clinical and biological endpoints. The development of quantitative imaging methods along with machine learning has enabled the opportunity to move data science research towards translation for more personalized cancer treatments. Accumulating evidence has indeed demonstrated that noninvasive advanced imaging analytics, that is, radiomics, can reveal key components of tumor phenotype for multiple three‐dimensional lesions at multiple time points over and beyond the course of treatment. These developments in the use of CT, PET, US, and MR imaging could augment patient stratification and prognostication buttressing emerging targeted therapeutic approaches. In recent years, deep learning architectures have demonstrated their tremendous potential for image segmentation, reconstruction, recognition, and classification. Many powerful open‐source and commercial platforms are currently available to embark in new research areas of radiomics. Quantitative imaging research, however, is complex and key statistical principles should be followed to realize its full potential. The field of radiomics, in particular, requires a renewed focus on optimal study design/reporting practices and standardization of image acquisition, feature calculation, and rigorous statistical analysis for the field to move forward. In this article, the role of machine and deep learning as a major computational vehicle for advanced model building of radiomics‐based signatures or classifiers, and diverse clinical applications, working principles, research opportunities, and available computational platforms for radiomics will be reviewed with examples drawn primarily from oncology. We also address issues related to common applications in medical physics, such as standardization, feature extraction, model building, and validation.

中文翻译：

---

放射组学的机器和深度学习方法。

放射组学是定量图像分析的一个新兴领域，旨在将大规模提取的成像信息与临床和生物学终点联系起来。定量成像方法和机器学习的发展使得有机会将数据科学研究转向转化为更个性化的癌症治疗。越来越多的证据确实表明，无创高级成像分析，即放射组学，可以在治疗过程之外的多个时间点揭示多个三维病变的肿瘤表型的关键组成部分。CT、PET、US 和 MR 成像使用的这些发展可以增强患者分层和预后，支持新兴的靶向治疗方法。最近几年，深度学习架构已经展示了其在图像分割、重建、识别和分类方面的巨大潜力。许多强大的开源和商业平台目前可用于进入放射组学的新研究领域。然而，定量成像研究很复杂，应遵循关键的统计原则以充分发挥其潜力。特别是放射组学领域，需要重新关注最佳研究设计/报告实践以及图像采集、特征计算和严格统计分析的标准化，以推动该领域的发展。在本文中，机器和深度学习作为主要计算工具的作用，用于构建基于放射组学的特征或分类器的高级模型，以及各种临床应用、工作原理、研究机会和可用的放射组学计算平台将通过主要来自肿瘤学的示例进行审查。我们还解决了与医学物理学中常见应用相关的问题，例如标准化、特征提取、模型构建和验证。