Advances on Imaging of Hepatocellular Carcinoma
A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Causes, Screening and Diagnosis".
Deadline for manuscript submissions: 15 July 2026
Special Issue Editor
Interests: hepatobiliary imaging; MR; CT; PET; liver cancer
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Advances in radiologic imaging have transformed the diagnosis, characterization and treatment assessment of hepatocellular carcinoma (HCC). Beyond conventional modalities such as ultrasonography (US), computed tomography (CT), magnetic resonance imaging (MRI) and positron emission tomography (PET), the integration of machine learning and deep learning–based analytic frameworks has ushered in a new era of precision imaging. These approaches enable automated lesion detection, enhanced image segmentation and prediction of histopathologic features, thereby improving the reproducibility and efficiency of HCC diagnosis.
Equally transformative is the emergence of radiomics, which extracts high-dimensional quantitative data from medical images to uncover patterns invisible to the human eye. Radiomics signatures, in combination with clinical and molecular information, have demonstrated promise in refining risk stratification, predicting treatment response and facilitating personalized therapeutic strategies. Furthermore, the development of imaging biomarkers—derived from both handcrafted radiomics features and deep learning models—has provided novel tools for assessing tumor heterogeneity, guiding immunotherapy or targeted therapies and monitoring longitudinal therapeutic outcomes with higher sensitivity.
This Special Issue aims to highlight these cutting-edge advances by emphasizing the synergy between imaging science and computational analytics. We welcome original research articles, comprehensive reviews and technical perspectives that explore the application of machine learning/deep learning, radiomics and imaging biomarkers in HCC. Submissions focusing on the integration of these techniques for early tumor detection, biologically driven risk stratification and objective assessment of treatment response are particularly encouraged.
Prof. Dr. Masakatsu Tsurusaki
Guest Editor
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- machine learning/deep learning
- radiomics
- imaging biomarkers
- hepatocellular carcinoma
- CT
- MRI
- PET/CT
- Gd-EOB-DTPA
- LI-RADS
- therapeutic response assessment
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