Abstract
Purpose of Review
To familiarize readers with recent updates and additions to the Liver Imaging and Reporting Data System (LI-RADS) v2018 for hepatocellular carcinoma surveillance, diagnosis, and treatment response assessment.
Recent Findings
US surveillance, diagnosis, and treatment response assessment algorithms are now incorporated into LI-RADS v2018. Updates to the diagnostic algorithm for CT and MRI include clarification of the LI-RADS appropriate population, revision of LR-5 criteria to match with those advocated by the American Association for Study of Liver Disease, new specific criteria for the LR-M category, and modification of the tumor in vein (TIV) category.
Summary
LI-RADS v2018 facilitates clear communication between radiologists and the rest of the health care team by standardizing imaging terminology, interpretation, and reporting. LI-RADS also enhances imaging quality by providing minimal technical requirements for hepatocellular carcinoma imaging. Recent updates address US surveillance, clarify terminology, and incorporate treatment response. With these updates, LI-RADS addresses the entire spectrum of hepatocellular carcinoma imaging from screening to treatment response, thereby further promoting its integration into practice.
Similar content being viewed by others
References
Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance
Elsayes KM, Kielar AZ, Agrons MM, Szklaruk J, Tang A, Bashir MR, et al. Liver imaging reporting and data system: an expert consensus statement. J Hepatocell Carcinoma. 2017;4:29–39.
Radiology, A.C.O Liver imaging reporting and data systems (LI-RADS) v 2018 core. 2017 12–2017 [cited 2018 01–29-18]; Available from: (https://www.acr.org/Clinical-Resources/Reporting-and-Data-Systems/LI-RADS/CT-MRI-LI-RADS-v2018).
Aube C, et al. EASL and AASLD recommendations for the diagnosis of HCC to the test of daily practice. Liver Int. 2017;37(10):1515–25.
Wald C, Russo MW, Heimbach JK, Hussain HK, Pomfret EA, Bruix J. New OPTN/UNOS policy for liver transplant allocation: standardization of liver imaging, diagnosis, classification, and reporting of hepatocellular carcinoma. Radiology. 2013;266(2):376–82.
Heimbach JK. Overview of the updated AASLD guidelines for the management of HCC. Gastroenterol Hepatol (N Y). 2017;13(12):751–3.
Benson AB 3rd. Hepatobiliary cancer. Clinical practice guidelines in oncology. J Natl Compr Cancer Netw. 2003;1(1):94–108.
Tang A, Hallouch O, Chernyak V, Kamaya A, Sirlin CB. Epidemiology of hepatocellular carcinoma: target population for surveillance and diagnosis. Abdom Radiol (NY). 2018;43(1):13–25.
Morgan TA, et al. US LI-RADS: ultrasound liver imaging reporting and data system for screening and surveillance of hepatocellular carcinoma. Abdom Radiol (NY). 2018;43(1):41–55.
Simmons O, Fetzer DT, Yokoo T, Marrero JA, Yopp A, Kono Y, et al. Predictors of adequate ultrasound quality for hepatocellular carcinoma surveillance in patients with cirrhosis. Aliment Pharmacol Ther. 2017;45(1):169–77.
Singal A, et al. Meta-analysis: surveillance with ultrasound for early-stage hepatocellular carcinoma in patients with cirrhosis. Aliment Pharmacol Ther. 2009;30(1):37–47.
Tzartzeva K, et al. Surveillance imaging and alpha fetoprotein for early detection of hepatocellular carcinoma in patients with cirrhosis: a meta-analysis. Gastroenterology. 2018;154(6):1706–18 e1.
Marks RM, Ryan A, Heba ER, Tang A, Wolfson TJ, Gamst AC, et al. Diagnostic per-patient accuracy of an abbreviated hepatobiliary phase gadoxetic acid-enhanced MRI for hepatocellular carcinoma surveillance. AJR Am J Roentgenol. 2015;204(3):527–35.
Besa C, Lewis S, Pandharipande PV, Chhatwal J, Kamath A, Cooper N, et al. Hepatocellular carcinoma detection: diagnostic performance of a simulated abbreviated MRI protocol combining diffusion-weighted and T1-weighted imaging at the delayed phase post gadoxetic acid. Abdom Radiol (NY). 2017;42(1):179–90.
Goossens N, Singal AG, King LY, Andersson KL, Fuchs BC, Besa C, et al. Cost-effectiveness of risk score-stratified hepatocellular carcinoma screening in patients with cirrhosis. Clin Transl Gastroenterol. 2017;8(6):e101.
• Fraum TJ, et al. Differentiation of hepatocellular carcinoma from other hepatic malignancies in patients at risk: diagnostic performance of the liver imaging reporting and data system version 2014. Radiology. 2018;286(1):158–72 Single-center retrospective study with pathological reference standard, evaluated the features of non-HCC malignancies in an at risk cohort to help define LR-M and TIV criteria for v2017.
Fowler KJ, Potretzke TA, Hope TA, Costa EA, Wilson SR. LI-RADS M (LR-M): definite or probable malignancy, not specific for hepatocellular carcinoma. Abdom Radiol (NY). 2018;43(1):149–57.
Santillan C, Chernyak V, Sirlin C. LI-RADS categories: concepts, definitions, and criteria. Abdom Radiol (NY). 2018;43(1):101–10.
Santillan C, Fowler K, Kono Y, Chernyak V. LI-RADS major features: CT, MRI with extracellular agents, and MRI with hepatobiliary agents. Abdom Radiol (NY). 2018;43(1):75–81.
Chernyak V, Santillan CS, Papadatos D, Sirlin CB. LI-RADS((R)) algorithm: CT and MRI. Abdom Radiol (NY). 2018;43(1):111–26.
Chernyak V, Tang A, Flusberg M, Papadatos D, Bijan B, Kono Y, et al. LI-RADS((R)) ancillary features on CT and MRI. Abdom Radiol (NY). 2018;43(1):82–100.
CORE, A.L.R. 2017; Available from: chrome-extension://oemmndcbldboiebfnladdacbdfmadadm/https://www.acr.org/-/media/ACR/Files/RADS/LI-RADS/LIRADS_2017_Core.pdf?la=en.
•• Tang A, et al. Evidence supporting LI-RADS major features for CT- and MR imaging-based diagnosis of hepatocellular carcinoma: a systematic review. Radiology. 2018;286(1):29–48 Systematic review that captures current evidence supporting LI-RADS major features and categories.
Zhang YD, Zhu FP, Xu X, Wang Q, Wu CJ, Liu XS, et al. Liver imaging reporting and data system: substantial discordance between CT and MR for imaging classification of hepatic nodules. Acad Radiol. 2016;23(3):344–52.
Fowler KJ, Tang A, Santillan C, Bhargavan-Chatfield M, Heiken J, Jha RC, et al. Interreader reliability of LI-RADS version 2014 algorithm and imaging features for diagnosis of hepatocellular carcinoma: a large international multireader study. Radiology. 2018;286(1):173–85.
Davenport MS, Khalatbari S, Liu PSC, Maturen KE, Kaza RK, Wasnik AP, et al. Repeatability of diagnostic features and scoring systems for hepatocellular carcinoma by using MR imaging. Radiology. 2014;272(1):132–42.
Becker AS, Barth BK, Marquez PH, Donati OF, Ulbrich EJ, Karlo C, et al. Increased interreader agreement in diagnosis of hepatocellular carcinoma using an adapted LI-RADS algorithm. Eur J Radiol. 2017;86:33–40.
Ehman EC, Behr SC, Umetsu SE, Fidelman N, Yeh BM, Ferrell LD, et al. Rate of observation and inter-observer agreement for LI-RADS major features at CT and MRI in 184 pathology proven hepatocellular carcinomas. Abdom Radiol (NY). 2016;41(5):963–9.
Sofue K, Sirlin CB, Allen BC, Nelson RC, Berg CL, Bashir MR. How reader perception of capsule affects interpretation of washout in hypervascular liver nodules in patients at risk for hepatocellular carcinoma. J Magn Reson Imaging. 2016;43(6):1337–45.
Bashir MR, Huang R, Mayes N, Marin D, Berg CL, Nelson RC, et al. Concordance of hypervascular liver nodule characterization between the organ procurement and transplant network and liver imaging reporting and data system classifications. J Magn Reson Imaging. 2015;42(2):305–14.
Choi, M.H., Park G.E., Oh S.N., Park M.Y., Rha S.E., Lee Y.J., Jung S.E., Choi J.I., Reproducibility of mRECIST in measurement and response assessment for hepatocellular carcinoma treated by transarterial chemoembolization. Acad Radiol, 2018.
Lencioni R, Montal R, Torres F, Park JW, Decaens T, Raoul JL, et al. Objective response by mRECIST as a predictor and potential surrogate end-point of overall survival in advanced HCC. J Hepatol. 2017;66(6):1166–72.
• Ronot M, et al. Comparison of the accuracy of AASLD and LI-RADS criteria for the non-invasive diagnosis of HCC smaller than 3cm. J Hepatol. 2017; Prospective study comparing diagnostic accuracy of AASLD and LI-RADS in at-risk cohort, helps establish high PPV/specificity for LR-5 criteria.
Kim YY, An C, Kim S, Kim MJ. Diagnostic accuracy of prospective application of the liver imaging reporting and data system (LI-RADS) in gadoxetate-enhanced MRI. Eur Radiol. 2018;28(5):2038–46.
Choi SH, et al. Liver imaging reporting and data system v2014 with gadoxetate disodium-enhanced magnetic resonance imaging: validation of LI-RADS category 4 and 5 criteria. Investig Radiol. 2016;51(8):483–90.
Darnell A, Forner A, Rimola J, Reig M, García-Criado Á, Ayuso C, et al. Liver imaging reporting and data system with MR imaging: evaluation in nodules 20 mm or smaller detected in cirrhosis at screening US. Radiology. 2015;275(3):698–707.
Cerny M, et al. LI-RADS for MR imaging diagnosis of hepatocellular carcinoma: performance of major and ancillary features. Radiology. 2018:171678.
Tanabe M, et al. Imaging outcomes of liver imaging reporting and data system version 2014 category 2, 3, and 4 observations detected at CT and MR imaging. Radiology. 2016;281(1):129–39.
Choi JY, Cho HC, Sun M, Kim HC, Sirlin CB. Indeterminate observations (liver imaging reporting and data system category 3) on MRI in the cirrhotic liver: fate and clinical implications. AJR Am J Roentgenol. 2013;201(5):993–1001.
Burke LM, et al. Natural history of liver imaging reporting and data system category 4 nodules in MRI. Abdom Radiol (NY). 2016;41(9):1758–66.
An C, Park S, Chung YE, Kim DY, Kim SS, Kim MJ, et al. Curative resection of single primary hepatic malignancy: liver imaging reporting and data system category LR-M portends a worse prognosis. AJR Am J Roentgenol. 2017;209(3):576–83.
Joo I, Lee JM, Lee SM, Lee JS, Park JY, Han JK. Diagnostic accuracy of liver imaging reporting and data system (LI-RADS) v2014 for intrahepatic mass-forming cholangiocarcinomas in patients with chronic liver disease on gadoxetic acid-enhanced MRI. J Magn Reson Imaging. 2016;44(5):1330–8.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interest
Kathryn J. Fowler and Elizabeth Hecht each declare no potential conflicts of interest. Ania Z. Kielar reports grants from General Electric, for liver MRI research. Amit G. Singal serves on the speakers bureau and as a consultant for Bayer. Claude B. Sirlin reports research grants from Bayer, GE, Philips, and Siemens; lab service agreements with Gilead, ICON, Intercept, Shire, Synageva, and VirtualScopics; consulting agreements with AMRA, Boehringer, Guerbet; and speaker’s bureau for Resoundant.
Human and Animal Rights and Informed Consent
This article does not contain any studies with human or animal subjects performed by any of the authors.
Additional information
This article is part of the Topical Collection on Hepatic Cancer
Rights and permissions
About this article
Cite this article
Fowler, K.J., Hecht, E., Kielar, A.Z. et al. LI-RADS v2018: a Primer and Update for Clinicians. Curr Hepatology Rep 17, 425–433 (2018). https://doi.org/10.1007/s11901-018-0441-7
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11901-018-0441-7