The following abstract is drawn from a recently published paper in Journal of Hepatocellular Carcinoma. We invite you to read the full paper and join the conversation, become a member of the Radiology News community to share your thoughts, ask questions, and engage with others around this work.
Authors: Ruizhi Fu1,2,*, Chen Gao1,2,*, Xinjing Lou1,2, Ziqing Han1,2, Yizhen He1,2, Chenye Zheng1,2, Zhuping Yu3, Hongsheng Chang1,2
Abstract
Background
Combining artificial intelligence (AI) with radiomics for primary liver cancer (PLC) enhances diagnostic precision, sharpens risk stratification, and facilitates personalized treatment. The study aims to conduct a bibliometric analysis of this field, explore its research status and emerging hotspots, and provide data support and academic insights for subsequent research.
Methods
A bibliometric analysis of 2890 publications on PLC, AI, and radiomics from 2008 to 2025 was performed, using data retrieved from the Web of Science Core Collection (WoSCC) and Scopus, followed by manual screening and deduplication. The finalized dataset was analyzed and visualized using tools such as VOSviewer, CiteSpace, and R to examine trends in annual publication counts, the geographic distribution of research, the institutions involved, journals, authors, references, and keywords.
Results
Publication output has increased rapidly since 2018. China (n = 1603, 55.47%) was the leading contributor, and Sun Yat-sen University (n = 186, 6.44%) was the most productive institution. Of all authors, Song, Bin (n = 42) was the most prolific author. Frontiers in Oncology and Radiology were identified as the most productive and influential journals, respectively. The most frequently occurring keywords were “hepatocellular carcinoma”, “deep learning”, and “magnetic resonance imaging”, while “image reconstruction”, “liver cancer classification”, and “deep supervision” have emerged as prominent recent research hotspots.
Conclusion
Applications of AI and radiomics in imaging for PLC are gaining increasing attention. Future trends are expected to focus on enhancing algorithmic accuracy and advancing clinical prediction of microvascular invasion, postoperative outcomes after hepatectomy, and the effectiveness of transarterial chemoembolization.
Read the full article: Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging | JHC | Dove Medical Press
1Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), Hangzhou, People’s Republic of China
2The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China
3Department of Radiology, Quzhou TCM Hospital at the Junction of Four Provinces Affiliated to Zhejiang Chinese Medical University, Quzhou, People’s Republic of China
*These authors contributed equally to this work
