Conventional and artificial intelligence-based imaging for biomarker discovery in chronic liver disease

Abstract

Chronic liver diseases, resulting from chronic injuries of various causes, lead to cirrhosis with life-threatening complications including liver failure, portal hypertension, hepatocellular carcinoma. A key unmet medical need is robust non-invasive biomarkers to predict patient outcome, stratify patients for risk of disease progression and monitor response to emerging therapies. Quantitative imaging biomarkers have already been developed, for instance, liver elastography for staging fibrosis or proton density fat fraction on magnetic resonance imaging for liver steatosis. Yet, major improvements, in the field of image acquisition and analysis, are still required to be able to accurately characterize the liver parenchyma, monitor its changes and predict any pejorative evolution across disease progression. Artificial intelligence has the potential to augment the exploitation of massive multi-parametric data to extract valuable information and achieve precision medicine. Machine learning algorithms have been developed to assess non-invasively certain histological characteristics of chronic liver diseases, including fibrosis and steatosis. Although still at an early stage of development, artificial intelligence-based imaging biomarkers provide novel opportunities to predict the risk of progression from early-stage chronic liver diseases toward cirrhosis-related complications, with the ultimate perspective of precision medicine. This review provides an overview of emerging quantitative imaging techniques and the application of artificial intelligence for biomarker discovery in chronic liver disease.

Keywords: Chronic liver disease; Deep learning; Elastography; Histo-pathological features; Machine learning; Pejorative evolution; Quantitative biomarkers; Radiomics.

Fig. 1

Concepts of deep learning. By analogy to human neurons, deep learning generally refers to neural networks. Input data are weighted based on their importance and undergo a non-linear transformation, called activation function, to result in an output. These input weights, or parameters, are computed and optimized to allow the model to reach the highest diagnostic performances by minimizing the loss error function through a process called back-propagation

Fig. 2

Magnetic resonance imaging-based quantitative biomarkers for steatosis (fat fraction), iron overload (R2*) and fibrosis (MR elastography)