1From 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.).
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 cancers that will integrate patient characteristics and quantitative imaging features, while accounting for imaging modality and contrast agent.