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Review
. 2025 Jul 14;20(1):109.
doi: 10.1186/s13014-025-02690-z.

Research trends of selective internal radiation therapy for liver cancer: a bibliometric analysis

Mingkun Liu  1 , Tong Xia  1 , Qianqian Xu  1 , Yanfeng Liu  2
Affiliations
Review

Research trends of selective internal radiation therapy for liver cancer: a bibliometric analysis

Mingkun Liu et al. Radiat Oncol. .

Abstract

Background: Liver cancer is a major contributor to cancer-related mortality. Selective internal radiation therapy (SIRT) for liver cancer has garnered increasing attention. This study aims to comprehensively describe the current status, hotspots and trends of SIRT.

Methods: Publications from January 1, 1994, to May 31, 2024, were retrieved from the Web of Science Core Collection. CiteSpace, VOSviewer, the Bibliometrix package from R software, and an online analytical platform were used for knowledge mapping analysis and visualization.

Results: A total of 2046 publications were retrieved from the Web of Science Core Collection. The number of publications related to SIRT for liver cancer has been continuously increasing, particularly since 2005. The United States led in publications, and a strong collaboration network was observed among countries, particularly within Europe and America. The Northwestern University had the highest number of publications in the field of SIRT. Co-citation analysis identified "predictive dosimetry" and "atezolizumab" as current focal points within the field. High-frequency keywords include "transarterial radioembolization", "Y90 microspheres", "hepatocellular carcinoma", while recent citation bursts are predominantly centered on "lung shunt fraction," "tumor dose," "systemic therapy," "radiation segmentectomy," "Holmium-166," etc. CONCLUSION: As a local treatment, SIRT exhibits significant promise for its future role in the management of hepatic malignancies. Numerous studies on SIRT are actively in progress. Our study suggests that radiation segmentectomy may emerge as an important treatment in the future. Moreover, personalized dosimetry, the combination of SIRT with immunotherapy, and the development of novel radioactive agents are identified as pivotal areas for future research.

Keywords: Bibliometric analysis; Bibliometrix; Citespace; Liver cancer; Radioembolization; Selective internal radiation therapy; VOSviewer.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study is a bibliometric analysis, with all primary data extracted from the Web of Science core Collection database. As the research did not involve original data from human subjects, it did not encompass any interventions, experiments, or surveys that could potentially impact personal privacy, rights, or welfare. So ethics approval and consent to participate are not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Identification and selection of records
Fig. 2
Fig. 2
The number of publications, citations and H-index about selective internal radiotherapy for liver cancer per year from 1994 to 2024
Fig. 3
Fig. 3
(A) The time-based distribution of publications on SIRT for liver cancer among the top 10 countries. Distribution and collaboration network of countries/regions, created using the online analytical platform (bibliometric.com) (B) and VOSviewer (C)
Fig. 4
Fig. 4
(A) Distribution and collaboration network of universities /institutions. (B) Dynamics and trends of universities and institutions over time. (C) Top 20 Institutions with the strongest citation bursts. (D) Distribution and collaboration network of authors. (D) Authors’ Production over Time
Fig. 5
Fig. 5
The dual-map overlay of journals. Citing publications (emerging research) and cited references (basic research) are grouped on the left and right sides. Circles denote journals, with its vertical and horizontal axes for publication volume and author count, respectively. Curved lines of different colors link the zones, showing citation volumes
Fig. 6
Fig. 6
(A) The map of co-citation references. Dependency Relationship (B) and timeline distribution (C) of co-citation reference clusters based on the log-likelihood ratio algorithm from CiteSpace. (D) Top 30 references with the strongest citation bursts
Fig. 7
Fig. 7
(A) The network map of top 70 keywords with 4 clusters. The density view and (B) the time view (C) of the map of keywords. (D) Top 25 terms with the strongest citation bursts
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