{"title":"解码乳腺癌成像趋势：通过文献计量学洞察人工智能和放射组学的作用。","authors":"Xinyu Wu, Yufei Xia, Xinjing Lou, Keling Huang, Linyu Wu, Chen Gao","doi":"10.1186/s13058-025-01983-1","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Radiomics and AI have been widely used in breast cancer imaging, but a comprehensive systematic analysis is lacking. Therefore, this study aims to conduct a bibliometrics analysis in this field to discuss its research status and frontier hotspots and provide a reference for subsequent research.</p><p><strong>Methods: </strong>Publications related to AI, radiomics, and breast cancer imaging were searched in the Web of Science Core Collection. CiteSpace plotted the relevant co-occurrence network according to authors and keywords. VOSviewer and Pajek were used to draw relevant co-occurrence maps according to country and institution. In addition, R was used to conduct bibliometric analysis of relevant authors, countries/regions, journals, keywords, and annual publications and citations based on the collected information.</p><p><strong>Results: </strong>A total of 2,701 Web of Science Core Collection publications were retrieved, including 2,486 articles (92.04%) and 215 reviews (7.96%). The number of publications increased rapidly after 2018. The United States of America (n = 17,762) leads in citations, while China (n = 902) leads in the number of publications. Sun Yat-sen University (n = 75) had the largest number of publications. Bin Zheng (n = 28) was the most published author. Nico Karssemeijer (n = 72.1429) was the author with the highest average citations. \"Frontiers in Oncology\" was the journal with the most publications, and \"Radiology\" had the highest IF. The keywords with the most frequent occurrence were \"breast cancer\", \"deep learning\", and \"classification\". The topic trends in recent years were \"explainable AI\", \"neoadjuvant chemotherapy\", and \"lymphovascular invasion\".</p><p><strong>Conclusion: </strong>The application of radiomics and AI in breast cancer imaging has received extensive attention. Future research hotspots may mainly focus on the progress of explainable AI in the technical field and the prediction of lymphovascular invasion and neoadjuvant chemotherapy efficacy in clinical application.</p>","PeriodicalId":49227,"journal":{"name":"Breast Cancer Research","volume":"27 1","pages":"29"},"PeriodicalIF":7.4000,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863798/pdf/","citationCount":"0","resultStr":"{\"title\":\"Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights.\",\"authors\":\"Xinyu Wu, Yufei Xia, Xinjing Lou, Keling Huang, Linyu Wu, Chen Gao\",\"doi\":\"10.1186/s13058-025-01983-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Radiomics and AI have been widely used in breast cancer imaging, but a comprehensive systematic analysis is lacking. Therefore, this study aims to conduct a bibliometrics analysis in this field to discuss its research status and frontier hotspots and provide a reference for subsequent research.</p><p><strong>Methods: </strong>Publications related to AI, radiomics, and breast cancer imaging were searched in the Web of Science Core Collection. CiteSpace plotted the relevant co-occurrence network according to authors and keywords. VOSviewer and Pajek were used to draw relevant co-occurrence maps according to country and institution. In addition, R was used to conduct bibliometric analysis of relevant authors, countries/regions, journals, keywords, and annual publications and citations based on the collected information.</p><p><strong>Results: </strong>A total of 2,701 Web of Science Core Collection publications were retrieved, including 2,486 articles (92.04%) and 215 reviews (7.96%). The number of publications increased rapidly after 2018. The United States of America (n = 17,762) leads in citations, while China (n = 902) leads in the number of publications. Sun Yat-sen University (n = 75) had the largest number of publications. Bin Zheng (n = 28) was the most published author. Nico Karssemeijer (n = 72.1429) was the author with the highest average citations. \\\"Frontiers in Oncology\\\" was the journal with the most publications, and \\\"Radiology\\\" had the highest IF. The keywords with the most frequent occurrence were \\\"breast cancer\\\", \\\"deep learning\\\", and \\\"classification\\\". The topic trends in recent years were \\\"explainable AI\\\", \\\"neoadjuvant chemotherapy\\\", and \\\"lymphovascular invasion\\\".</p><p><strong>Conclusion: </strong>The application of radiomics and AI in breast cancer imaging has received extensive attention. Future research hotspots may mainly focus on the progress of explainable AI in the technical field and the prediction of lymphovascular invasion and neoadjuvant chemotherapy efficacy in clinical application.</p>\",\"PeriodicalId\":49227,\"journal\":{\"name\":\"Breast Cancer Research\",\"volume\":\"27 1\",\"pages\":\"29\"},\"PeriodicalIF\":7.4000,\"publicationDate\":\"2025-02-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11863798/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Breast Cancer Research\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1186/s13058-025-01983-1\",\"RegionNum\":1,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"Medicine\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Breast Cancer Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1186/s13058-025-01983-1","RegionNum":1,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
摘要
背景：放射组学和人工智能在乳腺癌影像学中应用广泛，但缺乏全面系统的分析。因此，本研究旨在对该领域进行文献计量学分析，探讨其研究现状和前沿热点，为后续研究提供参考。方法：在Web of Science Core Collection中检索与人工智能、放射组学和乳腺癌影像学相关的出版物。CiteSpace根据作者和关键词绘制相关共现网络。使用VOSviewer和Pajek根据国家和机构绘制相关的共现图。此外，根据收集到的信息，利用R对相关作者、国家/地区、期刊、关键词、年度出版物和引文进行文献计量分析。结果：共检索到Web of Science核心文献2701篇，其中文章2486篇（92.04%），综述215篇（7.96%）。2018年以后，论文发表数量迅速增加。美国（n = 17,762）在引用数上领先，而中国（n = 902）在发表数上领先。中山大学（n = 75）发表论文最多。郑斌（n = 28）是发表论文最多的作者。Nico Karssemeijer （n = 72.1429）是平均被引次数最高的作者。发表最多的期刊是《肿瘤学前沿》（Frontiers in Oncology），影响因子最高的是《放射学》（Radiology）。出现频率最高的关键词是“乳腺癌”、“深度学习”和“分类”。近年来的话题趋势是“可解释的人工智能”、“新辅助化疗”和“淋巴血管侵袭”。结论：放射组学和人工智能在乳腺癌影像学中的应用已受到广泛关注。未来的研究热点可能主要集中在可解释AI在技术领域的进展，以及临床应用中对淋巴血管侵袭和新辅助化疗疗效的预测。
Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights.
Background: Radiomics and AI have been widely used in breast cancer imaging, but a comprehensive systematic analysis is lacking. Therefore, this study aims to conduct a bibliometrics analysis in this field to discuss its research status and frontier hotspots and provide a reference for subsequent research.
Methods: Publications related to AI, radiomics, and breast cancer imaging were searched in the Web of Science Core Collection. CiteSpace plotted the relevant co-occurrence network according to authors and keywords. VOSviewer and Pajek were used to draw relevant co-occurrence maps according to country and institution. In addition, R was used to conduct bibliometric analysis of relevant authors, countries/regions, journals, keywords, and annual publications and citations based on the collected information.
Results: A total of 2,701 Web of Science Core Collection publications were retrieved, including 2,486 articles (92.04%) and 215 reviews (7.96%). The number of publications increased rapidly after 2018. The United States of America (n = 17,762) leads in citations, while China (n = 902) leads in the number of publications. Sun Yat-sen University (n = 75) had the largest number of publications. Bin Zheng (n = 28) was the most published author. Nico Karssemeijer (n = 72.1429) was the author with the highest average citations. "Frontiers in Oncology" was the journal with the most publications, and "Radiology" had the highest IF. The keywords with the most frequent occurrence were "breast cancer", "deep learning", and "classification". The topic trends in recent years were "explainable AI", "neoadjuvant chemotherapy", and "lymphovascular invasion".
Conclusion: The application of radiomics and AI in breast cancer imaging has received extensive attention. Future research hotspots may mainly focus on the progress of explainable AI in the technical field and the prediction of lymphovascular invasion and neoadjuvant chemotherapy efficacy in clinical application.
期刊介绍：
Breast Cancer Research, an international, peer-reviewed online journal, publishes original research, reviews, editorials, and reports. It features open-access research articles of exceptional interest across all areas of biology and medicine relevant to breast cancer. This includes normal mammary gland biology, with a special emphasis on the genetic, biochemical, and cellular basis of breast cancer. In addition to basic research, the journal covers preclinical, translational, and clinical studies with a biological basis, including Phase I and Phase II trials.
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