Liver cancer is the sixth most detected tumor and the third leading cause of tumor death worldwide. Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with specific risk factors and a targeted population. Imaging plays a major role in the management of HCC from screening to post-therapy follow-up. In order to optimize the diagnostic-therapeutic management and using a universal report, which allows more effective communication among the multidisciplinary team, several classification systems have been proposed over time, and LI-RADS is the most utilized. Currently, LI-RADS comprises four algorithms addressing screening and surveillance, diagnosis on computed tomography (CT)/magnetic resonance imaging (MRI), diagnosis on contrast-enhanced ultrasound (CEUS) and treatment response on CT/MRI. The algorithm allows guiding the radiologist through a stepwise process of assigning a category to a liver observation, recognizing both major and ancillary features. This process allows for characterizing liver lesions and assessing treatment. In this review, we highlighted both major and ancillary features that could define HCC. The distinctive dynamic vascular pattern of arterial hyperenhancement followed by washout in the portal-venous phase is the key hallmark of HCC, with a specificity value close to 100%. However, the sensitivity value of these combined criteria is inadequate. Recent evidence has proven that liver-specific contrast could be an important tool not only in increasing sensitivity but also in diagnosis as a major criterion. Although LI-RADS emerges as an essential instrument to support the management of liver tumors, still many improvements are needed to overcome the current limitations. In particular, features that may clearly distinguish HCC from cholangiocarcinoma (CCA) and combined HCC-CCA lesions and the assessment after locoregional radiation-based therapy are still fields of research.
A Narrative Review on LI-RADS Algorithm in Liver Tumors: Prospects and Pitfalls / De Muzio, Federica; Grassi, Francesca; Dell'Aversana, Federica; Fusco, Roberta; Danti, Ginevra; Flammia, Federica; Chiti, Giuditta; Valeri, Tommaso; Agostini, Andrea; Palumbo, Pierpaolo; Bruno, Federico; Cutolo, Carmen; Grassi, Roberta; Simonetti, Igino; Giovagnoni, Andrea; Miele, Vittorio; Barile, Antonio; Granata, Vincenza. - In: DIAGNOSTICS. - ISSN 2075-4418. - 12:7(2022), p. 1655. [10.3390/diagnostics12071655]