Majumder Shweta, Katz Sharyn, Kontos Despina, Roshkovan Leonid
Department of Radiology, University of Pennsylvania Perelman School of Medicine, Philadelphia, PA 19104, United States.
BJR Open. 2023 Dec 12;6(1):tzad004. doi: 10.1093/bjro/tzad004. eCollection 2024 Jan.
Radiomics and artificial intelligence carry the promise of increased precision in oncologic imaging assessments due to the ability of harnessing thousands of occult digital imaging features embedded in conventional medical imaging data. While powerful, these technologies suffer from a number of sources of variability that currently impede clinical translation. In order to overcome this impediment, there is a need to control for these sources of variability through harmonization of imaging data acquisition across institutions, construction of standardized imaging protocols that maximize the acquisition of these features, harmonization of post-processing techniques, and big data resources to properly power studies for hypothesis testing. For this to be accomplished, it will be critical to have multidisciplinary and multi-institutional collaboration.
放射组学和人工智能有望提高肿瘤影像学评估的精准度，因为它们能够利用传统医学影像数据中所包含的数千种隐匿数字影像特征。尽管这些技术功能强大，但它们存在多种变异性来源，目前阻碍了其临床转化。为了克服这一障碍，需要通过跨机构统一影像数据采集、构建标准化影像协议以最大限度获取这些特征、统一后处理技术以及利用大数据资源来为假设检验的研究提供充足动力，从而控制这些变异性来源。要实现这一点，多学科和多机构合作至关重要。
