Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and...

Hepatocellular carcinoma (HCC), the predominant form of primary liver cancer, significantly threatens to global health. Despite considerable advances in diagnostic and therapeutic approaches in recent years, the prognosis for patients with HCC remains unsatisfactory. The emergence of artificial intelligence (AI), particularly deep learning technologies, offers new hope for improving the diagnosis and treatment of HCC. Researchers have extensively explored ways to integrate deep learning models into the clinical management of HCC patients, which provides a valuable foundation for developing more personalized treatment strategies. Compared with other detection methods, computed tomography (CT) has attracted significant research interest because of its comprehensive advantages, including wide availability and high resolution, making it well suited for AI-powered analysis. This review systematically integrates deep learning technologies for HCC based on CT imaging, while focusing primarily on tumor diagnosis, segmentation, treatment response prediction, and patient prognosis prediction. Moreover, we review popular deep learning networks in various fields and describe the advantages of these prevalent deep learning models for different applications. Furthermore, we discuss the outstanding challenges in applying deep learning to extract information from CT images for the diagnosis and treatment of HCC patients. These insights could provide guidance for subsequent studies.