Imaging diagnosis of hepatocellular carcinoma: LI-RADS

Liver cancer is the third most common cause of cancer related death worldwide, 90% being hepatocellular carcinoma (HCC) and about half of all HCCs estimated to occur in China. Imaging plays a pivotal role in the management of HCC. When stringent criteria are applied to at-risk populations, it enables HCCs to be diagnosed by imaging alone without further need of invasive histology confirmation. To optimize HCC imaging diagnosis and reporting, several systems have been proposed. The Liver Imaging Reporting and Data System (LI-RADS (R)) is currently the most comprehensive of these systems, providing guidance on all imaging-related aspects of HCC, from technique for acquisition, reporting, assessment of treatment response and management. For diagnosis, LI-RADS uses major and ancillary imaging features to assign hierarchical categories that communicate the relative probability of HCC to focal liver observations detected in patients at risk. Two LI-RADS algorithms yield high specificity and positive predictive value for HCC diagnosis on contrast enhanced ultrasound (CEUS), CT and MRI. The standardized lexicon and interpretation provided by LI-RADS also improve inter-reader agreement for imaging features and lesion categorization. Additionally, a LI-RADS treatment response algorithm (LR-TR) provide imaging criteria for assessment of response to locoregional therapy. LI-RADS is designed for universal adoption and in this review, we highlighted the most relevant aspects of LI-RADS for the diagnosis of HCC in clinical practice and discussed areas where LI-RADS and Asian guidelines are different.

Automated summary

LI-RADS plays a crucial role in HCC imaging diagnosis, utilizing major and ancillary imaging features to assign hierarchical categories, thereby improving specificity and positive predictive value for HCC diagnosis, while also enhancing consistency in interpreting imaging features and lesion categorization.