Quantitative contrast-enhanced ultrasound for the evaluation and early detection of hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) is the most common primary liver malignancy with high mortality mainly due to pre-existing liver disease and delay in diagnosis, limiting treatment options. Currently, the state-of-the-art diagnostic metric for HCC is LiRADS (Liver Imaging Reporting and Data System), a qualitative image assessment based on the bolus transit characteristics of microbubbles that stratifies liver tumors suspected for HCC. However, with contrast-enhanced ultrasound, liver tumor perfusion can be quantifiably characterized to identify abnormal arterial flow after an initial portal venous and hepatic arterial flow reduction in the liver, which are typical features of HCCs. The image intensity in a region of interest (ROI) in the HCC is used to form a time-intensity curve (TIC) of the bolus transit (wash-in/washout). A lognormal curve is fitted to the TIC and parameters related to blood flow in the tumor are extracted. Respiratory gating and motion compensation are applied to the video loops before forming the TIC to remove noise and improve the accuracy of the HCC perfusion measurements. Parametric images of the rise time and peak intensity provide an HCC classification algorithm. Utilizing this approach, we provide an improvement to the LiRADS method for an earlier and more accurate diagnosis of HCC.