Performance of Ultrasound Techniques and the Potential of Artificial Intelligence in the Evaluation of Hepatocellular Carcinoma and Non-Alcoholic Fatty Liver Disease.

Abstract

Simple SummaryThe increasing prevalence of non-alcoholic fatty liver disease (NAFLD) represents a challenge for the current medical systems. If NAFLD is left undetected and untreated, it can progress towards fibrosis, cirrhosis, and hepatocellular carcinoma (HCC). To date, ultrasonography (US) is the first-line examination indicated to NAFLD patients that also screens for other focal liver lesions (FLLs). The downside of conventional B-mode US is that it cannot accurately quantify steatosis and fibrosis and cannot further characterize FLL certainly—is it cancer or is it not? Ultrasound contrast agents (UCAs) allowed physicians to further evaluate the FLL for the diagnosis of HCC. This review discusses the performance of US techniques in NAFLD and NAFLD-related HCC diagnosis, as well as of artificial intelligence (AI)-based methods, specifically the usefulness and assistance of deep learning algorithms for improving liver US image processing.Global statistics show an increasing percentage of patients that develop non-alcoholic fatty liver disease (NAFLD) and NAFLD-related hepatocellular carcinoma (HCC), even in the absence of cirrhosis. In the present review, we analyzed the diagnostic performance of ultrasonography (US) in the non-invasive evaluation of NAFLD and NAFLD-related HCC, as well as possibilities of optimizing US diagnosis with the help of artificial intelligence (AI) assistance. To date, US is the first-line examination recommended in the screening of patients with clinical suspicion of NAFLD, as it is readily available and leads to a better disease-specific surveillance. However, the conventional US presents limitations that significantly hamper its applicability in quantifying NAFLD and accurately characterizing a given focal liver lesion (FLL). Ultrasound contrast agents (UCAs) are an essential add-on to the conventional B-mode US and to the Doppler US that further empower this method, allowing the evaluation of the enhancement properties and the vascular architecture of FLLs, in comparison to the background parenchyma. The current paper also explores the new universe of AI and the various implications of deep learning algorithms in the evaluation of NAFLD and NAFLD-related HCC through US methods, concluding that it could potentially be a game changer for patient care.