解读乳腺癌成像趋势：通过文献计量学见解看人工智能和放射组学的作用

Decoding breast cancer imaging trends: the role of AI and radiomics through bibliometric insights.

作者信息

Wu Xinyu, Xia Yufei, Lou Xinjing, Huang Keling, Wu Linyu, Gao Chen

机构信息

Department of Radiology, The First Affiliated Hospital of Zhejiang Chinese Medical University (Zhejiang Provincial Hospital of Chinese Medicine), 54 Youdian Road, Hangzhou, China.

The First School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, China.

出版信息

Breast Cancer Res. 2025 Feb 25;27(1):29. doi: 10.1186/s13058-025-01983-1.

DOI:10.1186/s13058-025-01983-1

PMID:40001088

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11863798/

Abstract

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.

摘要

背景

放射组学和人工智能已广泛应用于乳腺癌成像，但缺乏全面的系统分析。因此，本研究旨在对该领域进行文献计量学分析，以探讨其研究现状和前沿热点，并为后续研究提供参考。

方法

在科学网核心合集数据库中检索与人工智能、放射组学和乳腺癌成像相关的出版物。CiteSpace根据作者和关键词绘制相关的共现网络。VOSviewer和Pajek用于根据国家和机构绘制相关的共现图谱。此外，基于收集到的信息，使用R对相关作者、国家/地区、期刊、关键词以及年度出版物和被引频次进行文献计量学分析。

结果

共检索到2701篇科学网核心合集出版物，其中包括2486篇文章（92.04%）和215篇综述（7.96%）。2018年后出版物数量迅速增加。美国（n = 17762）在被引频次方面领先，而中国（n = 902）在出版物数量方面领先。中山大学（n = 75）的出版物数量最多。郑斌（n = 28）是发表文章最多的作者。尼科·卡尔斯梅杰尔（n = 72.1429）是平均被引频次最高的作者。《肿瘤前沿》是出版物数量最多的期刊，《放射学》的影响因子最高。出现频率最高的关键词是“乳腺癌”“深度学习”和“分类”。近年来的主题趋势是“可解释人工智能”“新辅助化疗”和“淋巴管侵犯”。