Liver Imaging Reporting and Data System: current status and future perspectives in the diagnosis of hepatocellular carcinoma
Year: 2026
Type: article
Abstract: Hepatocellular carcinoma (HCC) is the most common primary liver cancer and ranks third among cancer-related deaths globally, with over 900,000 new cases and approximately 830,000 deaths annually. Early detection is crucial, as 5-year survival exceeds 70% for lesions <3 cm treated curatively but drops below 20% in advanced stages. The Liver Imaging Reporting and Data System (LI-RADS) v2018, endorsed by major guidelines, provides a standardized framework for acquisition, interpretation, and reporting of liver lesions in high-risk patients. Using five major imaging features—non-rim arterial phase hyperenhancement, non-peripheral washout, enhancing capsule, lesion size, and threshold growth—alongside optional ancillary features, the LR-5 category achieves >95% positive predictive value for HCC. Meta-analyses of over 3300 observations report 86% sensitivity and 85% specificity for computed tomography/magnetic resonance imaging (MRI) LI-RADS, with gadoxetate-enhanced MRI reaching 88-91% sensitivity. Key limitations include overcalling benign hypervascular nodules, underdiagnosing hypovascular or well-differentiated HCC (up to 30% of lesions <2 cm), and misclassifying intrahepatic cholangiocarcinoma (CCA) or combined HCC-CCA as LR-5 (up to 40-50%). The LI-RADS Treatment Response Algorithm v2024 introduces criteria for radioembolization and stereotactic body radiation therapy, improving specificity for viable residual disease detection (93% vs. 86% for mRECIST). Future directions include artificial intelligence (82-90% accuracy), radiomics, multimodal imaging, and liquid biomarkers to reduce inter-reader variability and enhance prognostic stratification. Over a decade since its introduction, LI-RADS v2018 remains the reference standard for non-invasive HCC diagnosis and is evolving toward precision oncology.
Source: Italian Journal of Medicine
Language: English
Subfield: Hepatology
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W7124309641