Visualization Analysis of Research Hotspots and Trends in Treatment of Radioactive Iodine Refractory Differentiated Thyroid Carcinoma
10.3971/j.issn.1000-8578.2025.24.0826
- VernacularTitle:放射性碘难治性甲状腺癌治疗领域的研究热点及趋势可视化分析
- Author:
Xiaoxian ZENG
1
;
Hong ZHANG
1
Author Information
1. Department of Nuclear Medicine, Shenshan Medical Center, Memorial Hospital of Sun Yat-sen University, Shanwei 516600, China.
- Publication Type:CLINICALRESEARCH
- Keywords:
Radioactive iodine refractory differentiated thyroid cancer;
Treatment;
CiteSpace;
VOSviewer;
Bibliometrics
- From:
Cancer Research on Prevention and Treatment
2025;52(2):156-164
- CountryChina
- Language:Chinese
-
Abstract:
Objective To explore research hotspots and future development trends in radioactive iodine refractory differentiated thyroid carcinoma (RAIR-DTC) treatment from 2004 to 2024. Methods Literature on RAIR-DTC treatment published from January 2004 to May 2024 was retrieved from the Web of Science (WOS) database. CiteSpace, VOSviewer, and Microsoft Office Excel were used for visual analysis of publication volume, countries, institutions, authors, keywords, and co-citation networks. Results A total of 677 articles were included in the analysis. National and institutional co-occurrence analysis revealed that the United States, along with the MD Anderson Cancer Center at the University of Texas, was the most productive and influential in this field. Author and citation co-occurrence analysis highlighted the substantial contributions of Schlumberger M and Brose MS to the field. The exploration of high-frequency keywords and keyword clustering indicated tyrosine kinase inhibitors and disease prognostic factors were current research hotspots. Keyword burst analysis suggested that future research trends would focus on optimizing clinical benefits through reliable data provided from high-quality clinical trials and achieving personalized, precise treatment management. Conclusion Targeted drugs hold remarkable potential for RAIR-DTC treatment, and emphasizing predictive factors for disease prognosis offers valuable guidance for medical practice.
