Department of Medical Physics, Centro di Riferimento Oncologico di Aviano (CRO) IRCCS, Aviano, PN, 33081, Italy.
Department of Radiation Oncology, University of Michigan, Ann Arbor, MI, 48103, USA.
Med Phys. 2020 Jun;47(5):e185-e202. doi: 10.1002/mp.13678.
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.
放射组学是定量图像分析中的一个新兴领域，旨在将大规模提取的成像信息与临床和生物学终点相关联。定量成像方法的发展以及机器学习的应用，为将数据科学研究转化为更个性化的癌症治疗提供了机会。越来越多的证据确实表明，非侵入性的先进成像分析，即放射组学，可以揭示多个三维病变在治疗过程中的多个时间点上肿瘤表型的关键组成部分。这些在 CT、PET、US 和 MR 成像中的应用的发展可以增强患者分层和预后预测，支持新兴的靶向治疗方法。近年来，深度学习架构在图像分割、重建、识别和分类方面展示了巨大的潜力。目前有许多功能强大的开源和商业平台可用于开展放射组学的新研究领域。然而，定量成像研究很复杂，需要遵循关键的统计原则，才能充分发挥其潜力。放射组学领域尤其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，以使该领域向前发展。本文将回顾机器和深度学习作为基于放射组学特征或分类器的高级模型构建的主要计算工具的作用，以及其在各种临床应用中的工作原理、研究机会和可用的计算平台，并主要从肿瘤学方面举例说明。我们还解决了与医学物理学中的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。
