LI-RADS Treatment Response Algorithm: Performance and Diagnostic Accuracy.
Radiology. 2019 Jul;292(1):235-236. doi: 10.1148/radiol.2019190768. Epub 2019 Apr 30.
The Liver Imaging Reporting and Data System (LI-RADS) is a comprehensive system aiming to standardize performance, interpretation, and reporting of liver imaging findings, with prevailing emphasis on the imaging diagnosis of hepatocellular carcinoma (HCC) in patients at risk (1). In 2017, LI-RADS added an interpretation and reporting scheme for assessing the response of HCCs after local-regional therapies—a scheme that was based on existing understanding of imaging features after tumor ablation or embolization. Imaging findings after local-regional therapies guide clinical decision making by determining the likelihood of the presence or absence of viable tumor in a targeted tumor, which may require additional treatment. As a new algorithm, the LI-RADS Treatment Response, or LR-TR, algorithm has not been independently evaluated to determine its performance characteristics (2). In this issue of Radiology, Shropshire and colleagues report on the performance characteristics of the LI-RADS Treatment Response algorithm. These characteristics include a high positive predictive value, a high negative predictive value, and moderate interreader agreement based on retrospectively evaluated imaging findings in a cohort of patients with HCC treated with bland embolization prior to liver transplantation (3).
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