Deep learning in hepatocellular carcinoma: Current status and future perspectives

Abstract

Hepatocellular carcinoma (HCC) is among the leading causes of cancer incidence and death. Despite decades of research and development of new treatment options, the overall outcomes of patients with HCC continue to remain poor. There are areas of unmet need in risk prediction, early diagnosis, accurate prognostication, and individualized treatments for patients with HCC. Recent years have seen an explosive growth in the application of artificial intelligence (AI) technology in medical research, with the field of HCC being no exception. Among the various AI-based machine learning algorithms, deep learning algorithms are considered state-of-the-art techniques for handling and processing complex multimodal data ranging from routine clinical variables to high-resolution medical images. This article will provide a comprehensive review of the recently published studies that have applied deep learning for risk prediction, diagnosis, prognostication, and treatment planning for patients with HCC.

Keywords: Artificial intelligence; Deep learning; Hepatocellular carcinoma.

Figure 1

Schematic representation of the relationships between the terms artificial intelligence, machine learning, and deep learning, and how deep learning can utilize multimodal data to improve care for patients with hepatocellular carcinoma.