Department of Gastrointestinal Surgery, Tongji Hospital, Tongji Medical College in Huazhong University of Science and Technology, 430030 Wuhan, Hubei, China.
Department of General Surgery, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, 200240 Shanghai, China.
Front Biosci (Landmark Ed). 2022 Jul 18;27(8):224. doi: 10.31083/j.fbl2708224.
Breast cancer remains one of the leading malignancies in women with distinct clinical heterogeneity and intense multidisciplinary cooperation. Remarkable progresses have been made in artificial intelligence (AI). A bibliometric analysis was taken to characterize the current picture of development of AI in breast cancer.
Search process was performed in the Web of Science Core Collection database with analysis and visualization performed by R software, VOSviewer, CiteSpace and Gephi. Latent Dirichlet Allocation (LDA), a machine learning based algorithm, was used for analysis of topic terms.
A total of 511 publications in the field of AI in breast cancer were retrieved between 2000 to 2021. A total of 103 publications were from USA with 2482 citations, making USA the leading country in the field of AI in breast cancer, followed by China. Mem Sloan Kettering Canc Ctr, Radboud Univ Nijmegen, Peking Univ, Sichuan Univ, ScreenPoint Med BV, Lund Univ, Duke Univ, Univ Chicago, Harvard Med Sch and Univ Texas MD Anderson Canc Ctr were the leading institutions in the field of AI in breast cancer. AI, breast cancer and classification, mammography were the leading keywords. LDA topic modeling identified top fifty topics relating the AI in breast cancer. A total of five primary clusters were found within the network of fifty topics, including radiology feature, lymph node diagnosis and model, pathological tissue and image, dataset classification and machine learning, gene expression and survival.
This research depicted AI studies in breast cancer and presented insightful topic terms with future perspective.
乳腺癌仍然是女性中主要的恶性肿瘤之一，具有明显的临床异质性和强烈的多学科合作。人工智能（AI）取得了显著的进展。本研究采用文献计量学分析来描绘 AI 在乳腺癌中的发展现状。
在 Web of Science 核心合集数据库中进行检索过程，使用 R 软件、VOSviewer、CiteSpace 和 Gephi 进行分析和可视化。基于机器学习的算法——潜在狄利克雷分配（LDA）用于分析主题术语。
在 2000 年至 2021 年期间，共检索到 511 篇关于 AI 在乳腺癌领域的出版物。来自美国的出版物共有 103 篇，被引次数为 2482 次，使美国成为 AI 在乳腺癌领域的领先国家，其次是中国。Mem Sloan Kettering 癌症中心、拉德堡德大学、北京大学、四川大学、ScreenPoint Med BV、隆德大学、杜克大学、芝加哥大学、哈佛医学院和德克萨斯大学 MD 安德森癌症中心是 AI 在乳腺癌领域的领先机构。AI、乳腺癌和分类、乳房 X 线摄影是领先的关键词。LDA 主题建模确定了与 AI 相关的 50 个主题中的前 50 个主题。在这 50 个主题的网络中发现了五个主要的聚类，包括放射学特征、淋巴结诊断和模型、病理组织和图像、数据集分类和机器学习、基因表达和生存。
本研究描绘了 AI 在乳腺癌中的研究，并提出了具有未来展望的有见地的主题术语。
