Deep learning in hepatocellular carcinoma: Current status and future perspectives

Area of Science:

• Oncology and Medical Imaging Analysis
• Artificial Intelligence in Medicine

Background:

• Hepatocellular carcinoma (HCC) remains a significant global health challenge with poor patient outcomes.
• Current treatment options and risk prediction strategies for HCC have limitations, highlighting unmet needs.

Purpose of the Study:

• To provide a comprehensive review of deep learning applications in hepatocellular carcinoma research.
• To explore the use of AI in improving risk prediction, diagnosis, prognostication, and treatment planning for HCC.

Main Methods:

• Review of recently published studies utilizing deep learning algorithms.
• Analysis of AI applications across multimodal data, including clinical variables and medical images.

Main Results:

• Deep learning algorithms demonstrate state-of-the-art capabilities in processing complex medical data.
• Emerging AI applications show promise in enhancing various aspects of HCC management.

Conclusions:

• Deep learning is a rapidly advancing field with substantial potential to address unmet needs in hepatocellular carcinoma.
• Further research and application of AI are crucial for improving patient outcomes in HCC.