Wan Yonglin, Shen Yan, Wang Jun, Zhang Tingting, Fu Xiaohong
Department of Ultrasound, Gongli Hospital of Shanghai Pudong New Area, Shanghai, 200135, China.
Discov Oncol. 2025 Jul 1;16(1):1248. doi: 10.1007/s12672-025-03063-4.
OBJECTIVE: This 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. METHODS: The 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. RESULTS: A 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. CONCLUSION: This 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.
目的:本研究旨在运用文献计量学方法探索超声技术在三阴性乳腺癌(TNBC)中的应用。它呈现了一个可视化知识图谱以展示全球研究动态,并阐明该领域的研究方向、热点、趋势和前沿。 方法:使用科学网核心合集数据库,并采用CiteSpace和VOSviewer软件来可视化过去15年中与TNBC超声应用相关的年度发表量、合作网络(包括国家、机构和作者)、引用特征(如参考文献、共被引文献和出版物)以及关键词(包括出现情况和聚类)。 结果:共纳入310篇论文。第一篇论文于2010年发表,此后该领域的出版物数量真正开始增长,尤其是在2020年之后。中国在发表量方面成为领先国家,而上海交通大学在机构中产出最高。纪念斯隆凯特琳癌症中心被认为是该领域的关键研究机构。Adrada BE是发表论文数量最多的作者。Ko Es在作者中拥有最高的被引频次。关键词共现分析显示,频次最高的前三个关键词是“三阴性乳腺癌”“乳腺癌”和“超声检查”。时间线可视化表明,“乳腺癌”“推荐”“活检”“雌激素受体”和“放射组学”等聚类具有很强的时间连续性。出现值最高的关键词是“肿瘤”(6.80)。新兴术语的趋势分析表明,对“机器学习方法”“预后”和“分子亚型”的关注日益增加,“机器学习方法”目前已成为一个重要关键词。 结论:本研究对TNBC中超声技术应用的现状进行了系统分析。它强调“机器学习方法”已成为该研究领域当前及可预见未来的核心焦点和前沿。这些发现为超声技术在TNBC诊断和治疗中的应用提供了有价值的理论见解,并为TNBC相关医学影像研究的进一步发展奠定了坚实基础。
Front Med (Lausanne). 2024-11-22