Knowledge mapping of ultrasound technology and triple-negative breast cancer: a visual and bibliometric analysis

ObjectiveThis study aims to explore the application of ultrasound technology in triple-negative breast cancer (TNBC) using bibliometric methods. It presents a visual knowledge map to exhibit global research dynamics and elucidates the research directions, hotspots, trends, and frontiers in this field.MethodsThe Web of Science Core Collection database was used, and CiteSpace and VOSviewer software were employed to visualize the annual publication volume, collaborative networks (including countries, institutions, and authors), citation characteristics (such as references, co-citations, and publications), as well as keywords (including emergence and clustering) related to ultrasound applications in TNBC over the past 15 years.ResultsA total of 310 papers were included. The first paper was published in 2010, and after that, publications in this field really took off, especially after 2020. China emerged as the leading country in terms of publication volume, while Shanghai Jiao Tong University had the highest output among institutions. Memorial Sloan Kettering Cancer Center was recognized as a key research institution within this domain. Adrada BE was the most prolific author in terms of publication count. Ko Es held the highest citation frequency among authors. Co-occurrence analysis of keywords revealed that the top three keywords by frequency were "triple-negative breast cancer," "breast cancer," and "sonography." The timeline visualization indicated a strong temporal continuity in the clusters of "breast cancer," "recommendations," "biopsy," "estrogen receptor," and "radiomics." The keyword with the highest emergence value was "neoplasms" (6.80). Trend analysis of emerging terms indicated a growing focus on "machine learning approaches," "prognosis," and "molecular subtypes," with "machine learning approach" emerging as a significant keyword currently.ConclusionThis study provided a systematic analysis of the current state of ultrasound technology applications in TNBC. It highlighted that "machine learning methods" have emerged as a central focus and frontier in this research area, both presently and for the foreseeable future. The findings offer valuable theoretical insights for the application of ultrasound technology in TNBC diagnosis and treatment and establish a solid foundation for further advancements in medical imaging research related to TNBC.