Prognostication of Hepatocellular Carcinoma Using Artificial Intelligence.

Area of Science:

• Oncology
• Radiology
• Artificial Intelligence
• Medical Imaging

Background:

• Hepatocellular carcinoma (HCC) exhibits significant biological heterogeneity, impacting patient prognosis.
• Current HCC treatment relies on tumor burden, neglecting prognostic diversity due to heterogeneity.
• Traditional radiologic prognostication for HCC is subjective and suffers from inter-observer variability.

Purpose of the Study:

• To review the potential of artificial intelligence (AI) in improving hepatocellular carcinoma (HCC) prognostication.
• To highlight AI's role in overcoming limitations of traditional subjective imaging assessments.
• To discuss AI-based radiomics and deep learning for objective, quantitative tumor phenotype analysis.

Main Methods:

• Review of existing literature on AI applications in HCC prognostication.
• Focus on radiomics and deep learning for quantitative image feature extraction.
• Analysis of AI's capability to provide objective and comprehensive tumor characterization.

Main Results:

• AI, particularly radiomics, demonstrates potential for objective HCC prognostication.
• AI can analyze numerous quantitative image features for detailed tumor phenotype assessment.
• AI shows promise in predicting microvascular invasion, recurrence risk, and treatment response.

Conclusions:

• AI offers a promising, objective approach to HCC prognostication, surpassing traditional methods.
• AI-driven radiomics and deep learning can enhance personalized patient management for HCC.
• Further research is needed to explore AI's full potential, limitations, and future prospects in HCC.