From the Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (V.C., R.K.G.D.); Liver Imaging Group, Department of Radiology, University of California, San Diego, San Diego, Calif (K.J.F., C.S.S., C.B.S.); Department of Radiology, Stanford University Medical Center, Stanford, Calif (A.K.); Department of Medicine and Radiology, University of California, San Diego, San Diego, Calif (Y.K.); Department of Radiology, Radiation Oncology and Nuclear Medicine, Université de Montréal, Montréal, Canada (A.T.); Department of Radiology, Thomas Jefferson University Hospital, Philadelphia, Pa (D.G.M.); and Department of Radiology, Yale Medical School, New Haven, Conn (J.W.).
Radiology. 2023 Apr;307(1):e222801. doi: 10.1148/radiol.222801. Epub 2023 Feb 28.
Since its initial release in 2011, the Liver Imaging Reporting and Data System (LI-RADS) has evolved and expanded in scope. It started as a single algorithm for hepatocellular carcinoma (HCC) diagnosis with CT or MRI with extracellular contrast agents and has grown into a multialgorithm network covering all major liver imaging modalities and contexts of use. Furthermore, it has developed its own lexicon, report templates, and supplementary materials. This article highlights the major achievements of LI-RADS in the past 11 years, including adoption in clinical care and research across the globe, and complete unification of HCC diagnostic systems in the United States. Additionally, the authors discuss current gaps in knowledge, which include challenges in surveillance, diagnostic population definition, perceived complexity, limited sensitivity of LR-5 (definite HCC) category, management implications of indeterminate observations, challenges in reporting, and treatment response assessment following radiation-based therapies and systemic treatments. Finally, the authors discuss future directions, which will focus on mitigating the current challenges and incorporating advanced technologies. Tha authors envision that LI-RADS will ultimately transform into a probability-based system for diagnosis and prognostication of liver cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.
自 2011 年首次发布以来,肝脏影像报告和数据系统(LI-RADS)在不断发展和扩展其范围。它最初是一个用于 CT 或 MRI 检查的肝细胞癌(HCC)诊断的单一算法,使用细胞外对比剂,并发展成为一个涵盖所有主要肝脏成像方式和使用场景的多算法网络。此外,它还开发了自己的词汇、报告模板和补充材料。本文重点介绍了 LI-RADS 在过去 11 年中的主要成就,包括在全球范围内的临床护理和研究中的应用,以及在美国完全统一 HCC 诊断系统。此外,作者还讨论了当前知识上的差距,包括监测方面的挑战、诊断人群的定义、感知的复杂性、LR-5(明确 HCC)类别的有限敏感性、不确定观察结果的管理意义、报告方面的挑战以及放射治疗和系统治疗后治疗反应的评估。最后,作者讨论了未来的方向,将重点放在缓解当前的挑战和整合先进技术上。作者设想,LI-RADS 将最终转变为一种基于概率的肝癌诊断和预后系统,该系统将整合患者特征和定量成像特征,同时考虑成像方式和对比剂。