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

Abstract

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.

Keywords

Radiomics / Deep learning / Hepatocellular carcinoma / Precision medicine / Prognosis / Applications and challenges

Acknowledgements

None.

Funding

The study was supported by the Nanchong Bureau of Science and Technology (24YFZJZC0061).

Conflict of interest

The authors declare no competing interests.

Author contributions

Conceptualization: Haijun He, Yang Jing

Visualization: Mei Zhang, Rengui Chen, Tong Chen

Writing–original draft: Jing Zheng, Mingquan Luo

Writing – review & editing: Haijun He, Yang Jing

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Availability of data

Not applicable.

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