The application and challenges of artificial intelligence in imaging-based precision treatment of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer and the third leading cause of cancer-related deaths worldwide. Early diagnosis and effective treatment of HCC remain major global health challenges and place a significant burden on healthcare systems. In recent years, significant advancements in radiomics and deep learning (DL) technologies have been made in the field of medical imaging, offering new possibilities for the diagnosis, treatment, and prognostic assessment of HCC. This review summarizes the current applications of radiomics and DL in precision medicine for HCC, with a focus on their roles in diagnosis, pathological grading, prediction of microvascular invasion, post-operative recurrence, and treatment efficacy assessment. By analyzing existing studies, this paper demonstrates both the potential and current limitations of these technologies in HCC management and outlines directions for future development.