Hou Chenke, Huang Ting, Hu Keke, Ye Zhifeng, Guo Junhua, Zhou Heran
Hangzhou TCM Hospital of Zhejiang Chinese Medical University (Hangzhou Hospital of Traditional Chinese Medicine), Hangzhou, 310007, Zhejiang, China.
Department of Oncology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University, No. 453 Stadium Road, Xihu District, Hangzhou, 310007, Zhejiang, China.
Discov Oncol. 2025 Apr 16;16(1):537. doi: 10.1007/s12672-025-02329-1.
Breast cancer (BC) remains a leading cause of cancer-related mortality among women globally, with increasing incidence rates posing significant public health challenges. Recent advancements in artificial intelligence (AI) have revolutionized medical imaging, particularly in enhancing diagnostic accuracy and prognostic capabilities for BC. While multimodal imaging combined with AI has shown remarkable potential, a comprehensive analysis is needed to synthesize current research and identify emerging trends and hotspots in AI-assisted multimodal imaging for BC.
This study analyzed literature on AI-assisted multimodal imaging in BC from January 2010 to November 2024 in Web of Science Core Collection (WoSCC). Bibliometric and visualization tools, including VOSviewer, CiteSpace, and the Bibliometrix R package, were employed to assess countries, institutions, authors, journals, and keywords.
A total of 80 publications were included, revealing a steady increase in annual publications and citations, with a notable surge post-2021. China led in productivity and citations, while Germany exhibited the highest citation average. The United States demonstrated the strongest international collaboration. The most productive institution and author are Radboud University Nijmegen and Xi, Xiaoming. Publications were predominantly published in Computerized Medical Imaging and Graphics, with Qian, XJ's 2021 study on BC risk prediction under deep learning frameworks being the most influential. Keyword analysis highlighted themes such as "breast cancer", "classification", and "deep learning".
AI-assisted multimodal imaging has significantly advanced BC diagnosis and management, with promising future developments. This study offers researchers a comprehensive overview of current frameworks and emerging research directions. Future efforts are expected to focus on improving diagnostic precision and refining therapeutic strategies through optimized imaging techniques and AI algorithms, emphasizing international collaboration to drive innovation and clinical translation.
乳腺癌（BC）仍是全球女性癌症相关死亡的主要原因，发病率不断上升带来了重大的公共卫生挑战。人工智能（AI）的最新进展彻底改变了医学成像，特别是在提高乳腺癌的诊断准确性和预后能力方面。虽然多模态成像与人工智能相结合已显示出巨大潜力，但需要进行全面分析，以综合当前研究，并确定人工智能辅助的乳腺癌多模态成像的新兴趋势和热点。
本研究分析了2010年1月至2024年11月期间Web of Science核心合集（WoSCC）中关于人工智能辅助乳腺癌多模态成像的文献。使用文献计量学和可视化工具，包括VOSviewer、CiteSpace和Bibliometrix R包，来评估国家、机构、作者、期刊和关键词。
共纳入80篇出版物，显示年度出版物和引用量稳步增加，2021年后显著激增。中国在生产力和引用量方面领先，而德国的平均引用率最高。美国展示了最强的国际合作。产出最多的机构和作者分别是拉德堡德大学奈梅亨分校和Xi Xiaoming。出版物主要发表在《计算机化医学成像与图形学》上，Qian XJ 2021年关于深度学习框架下乳腺癌风险预测的研究最具影响力。关键词分析突出了“乳腺癌”、“分类”和“深度学习”等主题。
人工智能辅助的多模态成像显著推进了乳腺癌的诊断和管理，未来发展前景广阔。本研究为研究人员提供了当前框架和新兴研究方向的全面概述。未来的工作预计将集中在通过优化成像技术和人工智能算法提高诊断精度和完善治疗策略上，强调国际合作以推动创新和临床转化。
