Section of Nuclear Medicine, Department of Radiology, Leiden University Medical Center (LUMC), Leiden, the Netherlands -
Biomedical Photonic Imaging Group, University of Twente, Enschede, the Netherlands -
Q J Nucl Med Mol Imaging. 2020 Sep;64(3):278-290. doi: 10.23736/S1824-4785.20.03263-X. Epub 2020 May 12.
In recent years, radiomics, defined as the extraction of large amounts of quantitative features from medical images, has gained emerging interest. Radiomics consists of the extraction of handcrafted features combined with sophisticated statistical methods or machine learning algorithms for modelling, or deep learning algorithms that both learn features from raw data and perform modelling. These features have the potential to serve as non-invasive biomarkers for tumor characterization, prognostic stratification and response prediction, thereby contributing to precision medicine. However, especially in nuclear medicine, variable results are obtained when using radiomics for these purposes. Individual studies show promising results, but due to small numbers of patients per study and little standardization, it is difficult to compare and validate results on other datasets. This review describes the radiomic pipeline, its applications and the increasing role of artificial intelligence within the field. Furthermore, the challenges that need to be overcome to achieve clinical translation are discussed, so that, eventually, radiomics, combined with clinical data and other biomarkers, can contribute to precision medicine, by providing the right treatment to the right patient, with the right dose, at the right time.
近年来，放射组学作为从医学图像中提取大量定量特征的方法，受到了越来越多的关注。放射组学包括手工特征的提取，结合复杂的统计方法或机器学习算法进行建模，或深度学习算法，这些算法可以从原始数据中学习特征并进行建模。这些特征有可能成为肿瘤特征描述、预后分层和反应预测的非侵入性生物标志物，从而为精准医学做出贡献。然而，特别是在核医学中，当使用放射组学来实现这些目的时，会得到不同的结果。个别研究显示出有前景的结果，但由于每个研究中的患者数量较少，且标准化程度较低，因此难以在其他数据集上比较和验证结果。本文综述了放射组学的工作流程、应用以及人工智能在该领域中的作用不断增加。此外，还讨论了实现临床转化所需要克服的挑战，以便放射组学最终能够与临床数据和其他生物标志物相结合，通过为合适的患者提供合适的治疗、剂量和时间，为精准医学做出贡献。
