Clinical applications and recent advances in multimodal image fusion for hepatocellular carcinoma diagnosis: a narrative review
Year: 2026
Type: review
Abstract: Background and Objective: Hepatocellular carcinoma (HCC) remains a major cause of cancer-related mortality worldwide, and patient outcomes depend strongly on early detection, accurate staging, and timely treatment planning. However, single-modality imaging cannot fully characterize tumor heterogeneity because each modality provides only partial anatomical, functional, or metabolic information. Multimodal image fusion (MIF) addresses this limitation by integrating complementary data from ultrasound (US), computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET). This narrative review summarizes advances in MIF and its clinical applications in HCC. Methods: A targeted literature search of relevant databases was conducted, with a focus on recent and key English-language publications. The search strategy included terms related to "hepatocellular carcinoma", "CT", "MRI", "PET", "image registration", "image-guided ablation", and "artificial intelligence". Key Content and Findings: This review summarizes commonly used image-fusion strategies and recent advances in their clinical applications for HCC, including lesion detection, staging, ablation guidance, and post-treatment assessment. It also discusses the emerging role of artificial intelligence (AI) in image registration, fusion, and interpretation. MIF has shown clear clinical promise in supporting precision diagnosis and personalized management of HCC. Conclusions: Although MIF has shown clear clinical promise, its broader adoption remains limited by technical interoperability, cost, workflow complexity, and the need for standardized training. Future progress in system integration, algorithm robustness, and multidisciplinary collaboration may further expand its value in precision diagnosis and personalized management of HCC.
Institution: Anhui University of Science and Technology
Language: English
Subfield: Media Technology
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W7223385080