Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.
Authors
Affiliations (4)
Affiliations (4)
- Department of Artificial Intelligence in Diagnostic Radiology, The University of Osaka Graduate School of Medicine, 2-2, Yamadaoka, 565-0871, Suita, Japan. [email protected].
- Department of Artificial Intelligence in Diagnostic Radiology, The University of Osaka Graduate School of Medicine, 2-2, Yamadaoka, 565-0871, Suita, Japan.
- Department of Radiology, Kobe University Graduate School of Medicine, 7-5-2 Kusunoki-cho, Chuo-ku, 650-0017, Kobe, Japan.
- Department of Diagnostic and Interventional Radiology, The University of Osaka Graduate School of Medicine, 2-2, Yamadaoka, 565-0871, Suita, Japan.
Abstract
Liver cancer remains a significant global health concern, ranking as the sixth most common malignancy and the third leading cause of cancer-related deaths worldwide. Medical imaging plays a vital role in managing liver tumors, particularly hepatocellular carcinoma (HCC) and metastatic lesions. However, the large volume and complexity of imaging data can make accurate and efficient interpretation challenging. Artificial intelligence (AI) is recognized as a promising tool to address these challenges. Therefore, this review aims to explore the recent advances in AI applications in liver tumor imaging, focusing on key areas such as image reconstruction, image quality enhancement, lesion detection, tumor characterization, segmentation, and radiomics. Among these, AI-based image reconstruction has already been widely integrated into clinical workflows, helping to enhance image quality while reducing radiation exposure. While the adoption of AI-assisted diagnostic tools in liver imaging has lagged behind other fields, such as chest imaging, recent developments are driving their increasing integration into clinical practice. In the future, AI is expected to play a central role in various aspects of liver cancer care, including comprehensive image analysis, treatment planning, response evaluation, and prognosis prediction. This review offers a comprehensive overview of the status and prospects of AI applications in liver tumor imaging.