This article provides a glimpse into the future of the Liver Imaging Reporting and Data System (LI-RADS), discussing the immediate and long-term plans for its continuing improvement and expansion. To complement the Core and Essentials components of the latest version of LI-RADS, a comprehensive manual will be released soon, and it will include technical recommendations, management guidance, as well as reporting instructions and templates. In this article, we briefly review the process by which LI-RADS has been developed until now, a process guided by a variable combination of data, expert opinion, and desire for congruency with other diagnostic systems in North America. We then look forward, envisioning that forthcoming updates to LI-RADS will occur regularly every 3 to 5 years, driven by emerging high-quality scientific evidence. We highlight some of the key knowledge and technology gaps that will need to be addressed to enable the needed refinements. We also anticipate future expansions in scope to meet currently unaddressed clinical needs. Finally, we articulate a vision for eventual unification of imaging system for HCC screening and surveillance, diagnosis and staging, and treatment response assessment.

Chronic liver disease, irrespective of cause, can eventually lead to cirrhosis, which is the primary risk factor for developing hepatocellular carcinoma (HCC). In patients with cirrhosis or appropriate risk factors, HCC can be diagnosed by imaging with high specificity using liver imaging reporting and data system v2017, obviating the need for histologic confirmation. Confident recognition of cirrhosis by conventional imaging alone can be challenging, as radiologists are not always provided with the requisite information to determine if the patient has cirrhosis or other risk factors for HCC. Moreover, cirrhosis-associated abnormalities may impair the diagnostic accuracy of imaging for HCC. This article addresses the diagnosis of cirrhosis by non-invasive imaging and the implications of cirrhosis for imaging interpretation and accuracy.

Objective

The purposes of this article are to review a variety of pitfalls in liver imaging that can lead to the inaccurate diagnosis of focal hepatic lesions in cirrhosis, to describe the pathophysiologic processes of these pitfalls, and to provide specific clues for achieving the correct diagnoses.

Conclusion

Cirrhosis complicates liver imaging. The distortion and replacement of normal liver parenchyma by fibrous and regenerative tissue can change the typical appearance of many benign lesions, causing them to be misinterpreted as malignancy. In addition, the high incidence and prevalence of hepatocellular carcinoma among patients with cirrhosis put radiologists on high alert for any suspicious findings, especially because not all hepatocellular carcinomas have a typical imaging appearance.

The Liver Imaging Reporting and Data System (LI-RADS) standardizes performance of liver imaging in patients at risk for hepatocellular carcinoma (HCC) as well as interpretation and reporting of the results. Developed by experts in liver imaging and supported by the American College of Radiology, LI-RADS assigns to observations categories that reflect the relative probability of benignity, HCC, or other malignancy. While category assignment is based mainly on major imaging features, ancillary features may be applied to improve detection and characterization, increase confidence, or adjust LI-RADS categories. Ancillary features are classified as favoring malignancy in general, HCC in particular, or benignity. Those favoring malignancy in general or HCC in particular may be used to upgrade by a maximum of one category up to LR-4; those favoring benignity may be used to downgrade by a maximum of one category. If there are conflicting ancillary features (ie, one or more favoring malignancy and one or more favoring benignity), the category should not be adjusted. Ancillary features may be seen at diagnostic CT, MRI performed with extracellular agents, or MRI performed with hepatobiliary agents, with the exception of one ancillary feature assessed at US. This article focuses on LI-RADS version 2018 ancillary features seen at MRI. Specific topics include rules for ancillary feature application; definitions, rationale, and illustrations with clinical MRI examples; summary of evidence and diagnostic performance; pitfalls; and future directions. ^©RSNA, 2018.

The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system for standardizing the terminology, technique, interpretation, reporting, and data collection of liver imaging, created specifically for patients at risk for hepatocellular carcinoma. Over the past years, LI-RADS has been progressively implemented into clinical practice, but pitfalls remain related to user error and inherent limitations of the system. User pitfalls include the inappropriate application of LI-RADS to a low-risk patient population, incorrect measurement techniques, inaccurate assumptions about LI-RADS requirements, and improper usage of LI-RADS terminology and categories. System pitfalls include areas of discordance with the Organ Procurement and Transplantation Network (OPTN) as well as pitfalls related to rare ancillary features. This article reviews common user pitfalls in applying LI-RADS v2018 and how to avoid preventable errors and also highlights deficiencies of the current version of LI-RADS and how it might be improved in the future. Level of Evidence:3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019. J. Magn. Reson. Imaging 2019;50:1673-1686.

Computed tomography (CT) and magnetic resonance imaging (MRI) play critical roles for assessing treatment response of hepatocellular carcinoma (HCC) after locoregional therapy. Interpretation is challenging because posttreatment imaging findings depend on the type of treatment, magnitude of treatment response, time interval after treatment, and other factors. To help radiologists interpret and report treatment response in a clear, simple, and standardized manner, the Liver Imaging Reporting and Data System (LI-RADS) has developed a Treatment Response (LR-TR) algorithm. Introduced in 2017, the system provides criteria to categorize response of HCC to locoregional treatment (e.g., chemical ablation, energy-based ablation, transcatheter therapy, and radiation therapy). LR-TR categories include Nonevaluable, Nonviable, Equivocal, and Viable. LR-TR does not apply to patients on systemic therapies. This article reviews the LR-TR algorithm; discusses locoregional therapies for HCC, treatment concepts, and expected posttreatment findings; and illustrates LI-RADS treatment response assessment with CT and MRI.

The Liver Imaging Reporting and Data System (LI-RADS) is a reporting system created for the standardized interpretation of liver imaging findings in patients who are at risk for hepatocellular carcinoma (HCC). This system was developed with the cooperative and ongoing efforts of an American College of Radiology-supported committee of diagnostic radiologists with expertise in liver imaging and valuable input from hepatobiliary surgeons, hepatologists, hepatopathologists, and interventional radiologists. In this article, the 2017 version of LI-RADS for computed tomography and magnetic resonance imaging is reviewed. Specific topics include the appropriate population for application of LI-RADS; technical recommendations for image optimization, including definitions of dynamic enhancement phases; diagnostic and treatment response categories; definitions of major and ancillary imaging features; criteria for distinguishing definite HCC from a malignancy that might be non-HCC; management options following LI-RADS categorization; and reporting. ^©RSNA, 2017.

The increasing incidence and high morbidity and mortality of hepatocellular carcinoma (HCC) have inspired the creation of the Liver Imaging Reporting and Data System (LI-RADS). LI-RADS aims to reduce variability in exam interpretation, improve communication, facilitate clinical therapeutic decisions, reduce omission of pertinent information, and facilitate the monitoring of outcomes. LI-RADS is a dynamic process, which is updated frequently. In this article, we describe the LI-RADS 2014 version (v2014), which marks the second update since the initial version in 2011.