Trends of mapping knowledge structure and themes of cancer sonodynamic therapy: a text-mining study

Abstract

Background: Sonodynamic therapy (SDT) is a non-invasive cancer treatment technique stemming from photodynamic therapy (PDT) and has garnered escalated interest among researchers in recent years. Numerous aspects of cancer SDT remain contentious, and the global research trajectory within this domain remains insufficiently explored. This study seeks to delineate the comprehensive knowledge framework, developmental trends, and pivotal research focal points concerning cancer SDT.

Methods: The study retrieved documents on cancer SDT from the Web of Science Core Collection (WoSCC) database spanning from 1 January 2000 to 7 December 2023. Bibliometric visualization was carried out through the utilization of CiteSpace 6.2 R6, VOSviewer 1.6.20, and an online analytical platform. Several bibliometric techniques including co-authorship, co-citation, co-occurrence, cluster, as well as burst analysis were used.

Results: A total of 672 publications including 603 articles and 69 reviews were included. The annual publication count exhibited a steady increase over time, notably experiencing a surge, particularly in recent years. In terms of contributors, China has maintained its prominent position with the highest outputs and the most financial support. Chinese Academy of Sciences contributed the most articles. Materials Science was the most investigated research areas. Breast cancer emerged as the most extensively studied tumor, succeeded by sarcoma, hepatocellular carcinoma, melanoma, pancreatic cancer, glioma. According to co-cited references, "harnessing nanomaterial", "sonodynamic precision tumor therapy" and "metal-organic framework" denote the current and emerging research focuses within the field. In tandem with the results from keywords co-occurrence and burst, we identified the following research topics including mechanism of induced cell death (ferroptosis, immunogenic cell death), nano-related research (nanoplatform, nanozymes, nanomaterials, nanosheets, metal-organic frameworks (MOFs), nanocomposites, nanoparticles, nanosonosensitizers, liposomes, nanocarriers), combination therapies (chemodynamic therapy, immunotherapy, radiotherapy, photothermal therapy), and tumor microenvironment (hypoxia, singlet oxygen, oxidative stress), that may remain the research hotspots and receive sustained attention in the near future.

Conclusions: For the first time, this bibliometric analysis not only presents a comprehensive portrayal of the knowledge framework, but also delineates shifts in research focal points related to cancer SDT within the last two decades. This systematic summarization offers a comprehensive and lucid comprehension of cancer SDT, providing valuable insights for further investigations in this domain.

Keywords: Cancer; CiteSpace; VOSviewer; bibliometrics; sonodynamic therapy (SDT).

Figure 1

Graphical abstract of the study. WoSSC, Web of Science Core Collection; SCI, Science Citation Index; TI, title; AK, author keywords.

Figure 2

Data screening and collection procedures. WoSSC, Web of Science Core Collection; SCI, Science Citation Index; TI, title; AK, author keywords.

Figure 3

The annual publications and citations concerning cancer SDT. SDT, sonodynamic therapy.

Figure 4

Visual analysis of popular journals and research domains. (A) Dot plot of the top 10 journals by publication count. (B) Network visualization map of co-cited journals in cancer SDT research. Each node corresponds to a journal, with node size proportional to citation count. The line segments denote citation relationships, with closer proximity between nodes indicating stronger correlation, categorized by similar colors. (C) Top 10 research domains categorized by their respective publication counts. (D) Dual-map overlay of journals in the field of cancer SDT. IF, impact factor; SDT, sonodynamic therapy.

Figure 5

Contributions of countries/regions. (A) Top 10 leading countries/regions based on their publication outputs; (B) top 10 leading countries/regions based on their citations; (C) annual publication volume for the top 5 countries/regions; (D) international collaboration network among countries/regions; (E) VOSviewer analysis of country co-authorship.

Figure 6

Contributions of funding agencies and institutions. (A) Top 8 most active funding agencies involved in this domain; (B) dot plot of the top 10 institutions based on publication count; (C) organizational collaboration analysis by using CiteSpace; (D) top 10 institutions determined by their BC values (BC >0.1, red bar; BC <0.1 blue bar). BC, betweenness centrality.

Figure 7

Contributions of authors. (A) Co-authorship cluster visualization map generated by VOSviewer; (B) dot plot of the top 10 authors by publication count; (C) network visualization map of co-cited author; (D) top 10 authors based on TLS. TLS, total link strength.

Figure 8

Visual analysis of highly cited studies. (A) Network visualization map illustrating paper citation analysis; (B) the features of the top 10 highly cited studies within the cancer SDT research domain. SDT, sonodynamic therapy.

Figure 9

Visual analysis of co-cited references and reference burst. (A) CiteSpace-generated clusters of co-cited references. Each node in the representation signifies a reference, with nodes sharing the same color block indicating a cluster focused on a similar topic; (B) the size and average year of all 8 clusters; (C) CiteSpace produced the top 30 references exhibiting the most robust citation bursts. The timeline bars are depicted in blue, while the burst periods of the references are highlighted in red.

Figure 10

Keyword frequency analysis. (A) The density visualization map illustrates the analysis of keyword co-occurrence. This heat map represents keyword frequency through a spectrum of color gradients. Darker red hues denote active research areas with a higher frequency of keyword co-occurrence, while lighter yellow hues indicate less active areas with a lower frequency of keyword co-occurrence; (B) top 20 highest-frequency keywords; (C) top 10 tumors investigated in the realm of cancer SDT. SDT, sonodynamic therapy.

Figure 11

Trend analysis of keyword changes. (A) Overlay visualization map of keywords co-occurrence analysis. Each node symbolized a keyword, with the node’s size proportional to its frequency of occurrence. The node color corresponds to the respective AAY, as indicated by the color gradient in the lower right corner. Nodes shaded in blue signify keywords with earlier appearances, while those in red denote current research focuses. (B) Top 20 keywords with the highest AAY; (C) publication trend of nano-related studies in the field of cancer SDT; (D) top 20 keywords exhibiting the most robust citation bursts. AAY, average appearing year; SDT, sonodynamic therapy.