Liver Imaging Reporting and Data Systems (LI-RADS)

Improving diagnostic accuracy of Liver Imaging Reporting and Data System (LI-RADS) for diagnosing hepatocellular carcinoma

We are investigating the performance of LI-RADS diagnostic algorithms to combine imaging features to assign a LI-RADS category to individual liver observations in high-risk patients. Each LI-RADS category corresponds to a level of risk for hepatocellular carcinoma (HCC). For CT/MRI, this includes five major and 21 ancillary features whereas for contrast-enhanced ultrasound (CEUS), there are three major and five ancillary features.

Prior systematic reviews have explored the association of individual LI-RADS categories and individual LI-RADS imaging features with a diagnosis of HCC. Multiple important questions remain that we believe can be addressed using IPD meta-analysis. An IPD meta-analysis involves collecting and pooling de-identified primary study data from prior publications to increase study sample size permitting higher-level subgroup analysis. Increasingly, IPD meta-analyses are seen as the standard for evidence in many fields.

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Funding: Joan Sealy Trust for Cancer Research, Canadian Institute for Health Research (CIHR) Operating Grant, Radiological Society of North America (RSNA) Research Scholar Grant