ArticleJournal of hepatocellular carcinoma2026
Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.
Article in Journal of hepatocellular carcinoma, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
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. 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.
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Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.