Bibliometric analysis of research trends and collaboration patterns in cardio-oncology focusing on lung cancer and heart failure

Figures

• 

  Fig. 1. Global overview based on the search strategy in the field of LC and HF. A Core information of the literature included in this study. B Cumulative and annual number of publications from 2014 to 2024. C The yearly number of publications concerning LC and HF from 2014 to 2024 demonstrates a significant upward linear trend. SPSSAU [15] was employed to develop a trend analysis model for annual publications, using Poisson regression, which is a typical statistical approach for count data. The model’s validity was verified using a likelihood ratio test (χ²=43.573, df = 1, p < 0.001), showing that the model was statistically significant

• 

  Fig. 2. Worldwide partnerships in the study of LC and HF. A Country collaboration network visualized using R. B The top 10 countries ranked by the number of citations using R

• 

  Fig. 3. Analysis of institutional contributions and collaboration. A Institutional collaboration network mapped by CiteSpace. The size of the node reflects the institution’s publication count, the node’s color shows the time aspect (according to the color bar on the left), and the lines connecting them indicate collaborative relationships. B Institutional collaboration Network Visualization mapped by VOSviewer. Nodes symbolize institutions, and their size indicates the volume of publications. C The ten leading affiliations based on the number of publications using R

• 

  Fig. 4. Analysis of journal distribution and its impact. A Top 10 journals ranked by total citation index(TC). B Cumulative occurrences of publications in the top 10 most productive journals in the field from 2014 to 2024 conducted via Microsoft Excel

• 

  Fig. 5. Author collaboration network analysis. A A co-authorship network of authors created using VOSviewer consists of 63 clusters and 270 items. B A visualization of the core authors’ collaboration network density, created with VOSviewer, illustrates the collaborative landscape in LC and HF research

• 

  Fig. 6. Keyword analysis and research hotspot identification. A Keyword co-occurrence network analyzed with the Walktrap Clustering Algorithm in R. Keywords are represented by nodes, and lines show their co-occurrence relationships. B Duration and burst strength of the 15 leading keywords with the highest citation bursts. C A keyword co-occurrence network map created with VOSviewer shows the links between research themes at the intersection of LC and HF. Nodes symbolize keywords, with their size reflecting how often they appear and their color representing various clusters. D The keyword co-occurrence network density in C is visualized with VOSviewer, with color intensity showing the frequency of keyword co-occurrences, pointing out important research hotspots

• 

  Fig. 7. Co-citation analysis of journals and references. A Journal co-citation network visualized using VOSviewer. B Journal co-citation density visualization map using VOSviewer. C Co-citation network of the co-cited references (VOSviewer). D Density visualization map of the co-cited references (VOSviewer)

• 

  Fig. 8. Thematic evolution and conceptual structure in the field. A Keyword clusters of research topics performed with CiteSpace. With a modularity score of Q = 0.4358, the community structure is statistically significant (Q > 0.30), and the weighted mean silhouette coefficient of S = 0.7555 shows high intra-cluster homogeneity (S > 0.50). The color of each node signifies the year it initially appeared, from purple in 2014 to red in 2024, effectively visualizing the temporal progression of each thematic group. B Timeline of major research themes and topic evolution in LC and HF. C Thematic analysis of LC and HF was carried out using the Louvain Clustering Algorithm in R. The horizontal axis shows centrality, while the vertical axis indicates density. Topics in the first quadrant are well-developed, whereas those in the second quadrant are not crucial to the current field. The third quadrant includes topics that might have just appeared or could vanish soon, while the fourth quadrant consists of fundamental topics that lack significance. D A conceptual structure map of Keyword Plus using the Multiple Correspondence Analysis Method, showing semantic groupings. E Timeline view of keyword…