Li Xiao Tian, Huang Raymond Y
Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts, USA.
Neurooncol Adv. 2021 Jan 23;2(Suppl 4):iv49-iv55. doi: 10.1093/noajnl/vdaa054. eCollection 2020 Dec.
Radiomics is a novel technique in which quantitative phenotypes or features are extracted from medical images. Machine learning enables analysis of large quantities of medical imaging data generated by radiomic feature extraction. A growing number of studies based on these methods have developed tools for neuro-oncology applications. Despite the initial promises, many of these imaging tools remain far from clinical implementation. One major limitation hindering the use of these models is their lack of reproducibility when applied across different institutions and clinical settings. In this article, we discuss the importance of standardization of methodology and reporting in our effort to improve reproducibility. Ongoing efforts of standardization for neuro-oncological imaging are reviewed. Challenges related to standardization and potential disadvantages in over-standardization are also described. Ultimately, greater multi-institutional collaborative effort is needed to provide and implement standards for data acquisition and analysis methods to facilitate research results to be interoperable and reliable for integration into different practice environments.
放射组学是一种从医学图像中提取定量表型或特征的新技术。机器学习能够分析通过放射组学特征提取生成的大量医学影像数据。越来越多基于这些方法的研究已经开发出用于神经肿瘤学应用的工具。尽管最初有诸多前景，但这些成像工具中的许多仍远未实现临床应用。阻碍这些模型使用的一个主要限制是，当应用于不同机构和临床环境时，它们缺乏可重复性。在本文中，我们讨论了方法标准化和报告在提高可重复性方面的重要性。回顾了神经肿瘤学成像正在进行的标准化努力。还描述了与标准化相关的挑战以及过度标准化的潜在弊端。最终，需要更大规模的多机构协作努力，以提供并实施数据采集和分析方法的标准，以便促进研究结果具有可互操作性且可靠，从而能够整合到不同的实践环境中。
