The Role of Artificial Intelligence in the Detection and Implementation of Biomarkers for Hepatocellular Carcinoma: Outlook and Opportunities

Abstract

Liver cancer is a leading cause of cancer-related death worldwide, and its early detection and treatment are crucial for improving morbidity and mortality. Biomarkers have the potential to facilitate the early diagnosis and management of liver cancer, but identifying and implementing effective biomarkers remains a major challenge. In recent years, artificial intelligence has emerged as a promising tool in the cancer sphere, and recent literature suggests that it is very promising in facilitating biomarker use in liver cancer. This review provides an overview of the status of AI-based biomarker research in liver cancer, with a focus on the detection and implementation of biomarkers for risk prediction, diagnosis, staging, prognostication, prediction of treatment response, and recurrence of liver cancers.

Keywords: artificial intelligence; biomarkers; deep learning; hepatocellular carcinoma; liver cancer; machine learning.

Figure 1

Schematic representation of the role of artificial intelligence in the diagnosis of hepatocellular carcinoma. Abbreviations: CT, computed tomography; MRI, magnetic resonance imaging; NAFL, nonalcoholic fatty liver disease; NASH, nonalcoholic steatohepatitis; miRNA, microribonucleic acid; AI, artificial intelligence; ML, machine learning. Created with BioRender.com, accessed on 13 March 2023.