Topic: New Imaging Perspectives for HCC in the Era of Personalized Medicine and Artificial Intelligence

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related mortality worldwide. Despite rapid advances in systemic therapies and surgical techniques, clinical outcomes are still limited by the tumor’s heterogeneity, high recurrence rates, and suboptimal early diagnostic accuracy. Medical imaging—including computed tomography (CT), magnetic resonance imaging (MRI), ultrasound (US), positron emission tomography–CT (PET-CT), and PET-MRI—plays a pivotal role across all major stages of HCC management, from detection, characterization, and tumor staging to treatment response assessment and prognosis prediction.

With the advent of artificial intelligence (AI), medical imaging is undergoing a paradigm shift toward more quantitative, intelligent, and individualized decision making. Deep learning, vision-language models (VLMs), and agentic AI systems now enable multimodal integration of imaging, clinical, and pathological data, allowing for more precise delineation of tumor biology and the tumor microenvironment. Furthermore, radiomics and habitat imaging analysis provide novel technical methods to decode intratumoral heterogeneity and link imaging phenotypes with molecular subtypes and therapeutic responses.

This Special Issue focuses on commonly used multimodal liver imaging modalities (CT, MRI, US, PET-CT, and PET-MRI) in clinical practice and emerging AI-driven paradigms for HCC imaging, with particular emphasis on diagnostic accuracy, interpretability, and clinical reliability. It aims to highlight translational advances that bridge imaging science, computational technologies, and personalized HCC management. By integrating imaging biomarkers, this issue seeks to redefine how liver imaging data inform diagnosis, risk stratification, and treatment optimization for patients with HCC.

We welcome original research articles, reviews, mini-reviews, clinical trials, case reports, commentaries, methodological studies, and perspectives focused on, but not limited to, the following topics:

Recent advances in AI for HCC imaging;

1. Novel imaging and AI-based strategies for HCC screening in high-risk populations;

2. Innovative imaging technologies and methods for precision diagnosis of HCC;

3. Non-invasive imaging-based pathological subtyping of HCC;

4. Imaging biomarkers of aggressive biological behavior and molecular traits in HCC;

5. Imaging insights into the tumor microenvironment and immunometabolic landscape of HCC;

6. Imaging-based assessment of therapeutic response and prognosis in HCC;

7. AI-driven integration of imaging, pathology, and molecular multi-omics data;

8. Multimodal imaging fusion and cross-scale integration in HCC management;

9. Development of intelligent clinical decision-support systems for patients with HCC.

We cordially invite submissions spanning basic, translational, and clinical studies on microbiome-related mechanisms, diagnostics, and therapies in HCC.

Hepatocellular carcinoma, medical imaging, artificial intelligence, deep learning, radiomics, tumor microenvironment, imaging biomarkers, therapeutic response, multimodal imaging fusion