ArticleDiscover oncology2025
From texture analysis to artificial intelligence: global research landscape and evolutionary trajectory of radiomics in hepatocellular carcinoma.
Article in Discover oncology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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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
4 citing papers in PubMed.
- A bibliometric analysis of research trends and emerging frontiers in heat shock proteins and hepatocellular carcinoma from 2015 to 2025.Discover oncology · 2026Article
- Artificial Intelligence and Radiomics in Primary Liver Cancer Imaging: A Bibliometric and Visualized Analysis.Journal of hepatocellular carcinoma · 2026Article
- Applications of artificial intelligence and machine learning models in the prognosis and diagnosis of ovarian cancer.Frontiers in oncology · 2026Review
- Performance of AI-based machine learning models for overall survival prediction in advanced hepatocellular carcinoma patients receiving immunoradiotherapy.Frontiers in pharmacology · 2025Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
backgroundHepatocellular carcinoma (HCC) poses a substantial global health burden with high morbidity and mortality rates. Radiomics, which extracts quantitative features from medical images to develop predictive models, has emerged as a promising non-invasive approach for HCC diagnosis and management. However, comprehensive analysis of research trends in this field remains limited.
methodsWe conducted a systematic bibliometric analysis of radiomics applications in HCC using literature from the Web of Science Core Collection (January 2006-April 2025). Publications were analyzed using CiteSpace, VOSviewer, R, and Python scripts to evaluate publication patterns, citation metrics, institutional contributions, keyword evolution, and collaboration networks.
resultsAmong 906 included publications, we observed exponential growth, particularly accelerating after 2019. A global landscape analysis revealed China as the leader in publication volume, while the USA acted as the primary international collaboration hub. Countries like South Korea and the UK demonstrated higher average citation impact. Sun Yat-sen University was the most productive institution. Research themes evolved from fundamental texture analysis and CT/MRI applications toward predicting microvascular invasion, assessing treatment response (especially TACE), and prognostic modeling, driven recently by the deep integration of artificial intelligence (AI) and deep learning. Co-citation analysis revealed core knowledge clusters spanning radiomics methodology, clinical management, and landmark applications, demonstrating the field's interdisciplinary nature.
conclusionRadiomics in HCC represents a rapidly expanding, AI-driven field characterized by extensive multidisciplinary collaboration. Future priorities should emphasize standardization, large-scale multicenter validation, enhanced international cooperation, and clinical translation to maximize radiomics' potential in precision HCC oncology.
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What Socratic 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 Socratic graph.