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二十年来人工智能在乳腺癌诊断与治疗方面的研究趋势。

Research trends on AI in breast cancer diagnosis, and treatment over two decades.

作者信息

Singh Alok, Singh Akanksha, Bhattacharya Sudip

机构信息

Department of Community Medicine, Shree Guru Gobind Singh Tricentenary (SGT) University, Gurugram, Haryana, India.

Mahatma Gandhi Kashi Vidyapith (MGKV), Varanasi, Uttar Pradesh, India.

出版信息

Discov Oncol. 2024 Dec 18;15(1):772. doi: 10.1007/s12672-024-01671-0.

DOI:10.1007/s12672-024-01671-0
PMID:39692996
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11655727/
Abstract

OBJECTIVE: Recently, the integration of Artificial Intelligence (AI) has significantly enhanced the diagnostic accuracy in breast cancer screening. This study aims to deliver an extensive review of the advancements in AI for breast cancer diagnosis and prognosis through a bibliometric analysis. METHODOLOGY: Therefore, this study gathered pertinent peer-reviewed research articles from the Scopus database, spanning the years 2000 to 2024. These articles were subsequently subjected to quantitative analysis and visualization through the Bibliometrix R package. Ultimately, potential areas for future research challenges were pinpointed. RESULTS: This study analyzes the development of Artificial Intelligence (AI) research for breast cancer diagnosis and prognosis from 2000 to 2024, based on 2678 publications sourced from Scopus. A sharp rise in global publication trends is observed between 2018 and 2023, with 2023 producing 456 papers, indicating intensified academic focus. Leading contributors include ZHENG B, with 36 publications, and institutions like RADBOUD UNIVERSITY MEDICAL CENTER and the IEO EUROPEAN INSTITUTE OF ONCOLOGY IRCCS. The USA leads both in publications (473) and total citations (18,530), followed by India with 289 papers. Co-occurrence analysis shows that "mammography" (3171 occurrences) and "artificial intelligence" (1691 occurrences) are among the most frequent keywords, reflecting core themes. Co-citation network analysis identifies foundational works by authors like Lecun Y. and Simonyan K. in advancing AI applications in breast cancer. Institutional and country-level collaboration analysis reveals the USA's significant partnerships with China, the UK, and Canada, driving the global research agenda in this field. CONCLUSION: In conclusion, this bibliometric review underscores the growing influence of AI, particularly deep learning, in breast cancer diagnosis and treatment research from 2000 to 2024. The United States leads the field in publications and collaborations, with India, Spain, and the Netherlands also making significant contributions. Key institutions and journals have driven advancements, with AI applications focusing on improving diagnostic imaging and early detection. However, challenges like data limitations, regulatory hurdles, and unequal global collaboration persist, requiring further interdisciplinary efforts to enhance AI integration in clinical practice.

摘要

目的：近年来，人工智能（AI）的融入显著提高了乳腺癌筛查的诊断准确性。本研究旨在通过文献计量分析，全面回顾人工智能在乳腺癌诊断和预后方面的进展。 方法：因此，本研究从Scopus数据库中收集了2000年至2024年期间相关的同行评审研究文章。随后，通过Bibliometrix R软件包对这些文章进行定量分析和可视化处理。最终，确定了未来研究挑战的潜在领域。 结果：本研究基于从Scopus获取的2678篇出版物，分析了2000年至2024年期间人工智能在乳腺癌诊断和预后方面的研究发展情况。2018年至2023年间，全球出版物趋势急剧上升，2023年发表了456篇论文，表明学术关注度不断提高。主要贡献者包括郑B（36篇出版物），以及拉德堡德大学医学中心和欧洲肿瘤研究所IRCCS等机构。美国在出版物数量（473篇）和总被引次数（18530次）方面均居首位，其次是印度，有289篇论文。共现分析表明，“乳腺钼靶摄影”（出现3171次）和“人工智能”（出现1691次）是最常见的关键词，反映了核心主题。共被引网络分析确定了Y. Lecun和K. Simonyan等作者在推进人工智能在乳腺癌中的应用方面的基础著作。机构和国家层面的合作分析揭示了美国与中国、英国和加拿大的重要伙伴关系，推动了该领域的全球研究议程。 结论：总之，这篇文献计量学综述强调了2000年至2024年期间人工智能，特别是深度学习，在乳腺癌诊断和治疗研究中的影响力不断增强。美国在出版物和合作方面领先，印度、西班牙和荷兰也做出了重大贡献。关键机构和期刊推动了研究进展，人工智能应用主要集中在改善诊断成像和早期检测方面。然而，数据限制、监管障碍和全球合作不平等之类的挑战依然存在，需要进一步开展跨学科努力，以加强人工智能在临床实践中的整合。

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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/6d1024e04622/12672_2024_1671_Fig3_HTML.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/f4adfc3bcd57/12672_2024_1671_Fig5_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/f6149d19f0dc/12672_2024_1671_Fig6_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/0d838b69d12d/12672_2024_1671_Fig7_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/b8bfb1a9f952/12672_2024_1671_Fig8_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/e53c7a241075/12672_2024_1671_Fig9_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/c63e52eec779/12672_2024_1671_Fig10_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/f90b48e24502/12672_2024_1671_Fig11_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/b8b474eb429e/12672_2024_1671_Fig12_HTML.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b76/11655727/cd9c6d493e7c/12672_2024_1671_Fig13_HTML.jpg

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