From imaging to intelligence: AI applications in CT and MRI for hepatocellular carcinoma.
Authors
Affiliations (4)
Affiliations (4)
- Division of Gastroenterology and Hepatology, Department of Medicine, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
- School of Medicine, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
- Institute of Clinical Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan, ROC.
- Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan, ROC.
Abstract
Hepatocellular carcinoma (HCC) remains a leading cause of cancer-related mortality worldwide, largely due to high recurrence rates after curative treatment and poor prognosis in advanced-stage disease. Multiphasic computed tomography (CT) and magnetic resonance imaging (MRI) play central roles in the noninvasive diagnosis and management of HCC; however, diagnostic variability and limited sensitivity for early-stage and atypical lesions continue to pose major clinical challenges. In recent years, artificial intelligence (AI) has emerged as a powerful tool for enhancing medical image analysis and improving clinical decision-making in liver oncology. This review summarizes recent advances in AI-based applications for HCC imaging using CT and MRI. We discuss the roles of AI in lesion detection and segmentation, differential diagnosis and tumor classification, imaging-based biological characterization, and prediction of treatment response and prognosis. In particular, radiomics and deep learning approaches have enabled automated extraction of high-dimensional imaging features, facilitating noninvasive assessment of tumor aggressiveness and heterogeneity. The integration of multimodal data, including imaging, clinical, and molecular information, further enhances predictive performance and personalized risk stratification. Despite encouraging results, most existing studies are limited by retrospective designs, heterogeneous methodologies, and insufficient external validation. Challenges related to model interpretability, data standardization, and clinical integration also remain unresolved. We highlight current limitations and future research directions, emphasizing the need for multi-center collaboration, prospective validation, and development of transparent and regulatory-compliant systems. With continued technological innovation and interdisciplinary collaboration, AI-driven imaging analysis is expected to play an increasingly important role in precision medicine and outcome optimization for patients with HCC.