A Bibliometric Study of Oncology Imaging Diagnosis Based on Convolutional Neural Networks
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摘要:目的
通过分析十年来国内外发表的关于卷积神经网络的肿瘤影像诊断领域的文献特征，了解该领域的研究热点及发展趋势。
方法以SCI-E数据库为数据源，检索2012年—2022年十年间发表的有关卷积神经网络的肿瘤影像诊断领域的文献，利用CiteSpace软件分析文献的国家、机构、期刊、作者共被引和关键词的分布特征。
结果最终共有1088篇文献纳入研究；文献主要来自中国、美国和印度等国家；中山大学发文39篇，是发文量最多的研究机构；Radiology Nuclear Medicine Medical Imaging是发文量最多的期刊；共得到25个高频关键词和15个突发性关键词；形成了image segmentation、lung nodule等12个作者共被引聚类和automatic segmentation、breast cancer等11个关键词聚类。
结论当前卷积神经网络的肿瘤影像诊断的研究主要集中在肿瘤分割、肺结节识别、乳腺癌的辅助诊断以及其他高频肿瘤的研究。
Abstract:ObjectiveTo understand the research hotspots and research trends about convolutional neural networks in the field of oncology imaging diagnosis by analyzing the characteristics of published literature at home and abroad over the past decade.
MethodsThe SCI-E database was used as the data source to retrieve literature about convolutional neural networks in the field of oncology imaging diagnosis published from 2012 to 2022. The distribution characteristics of countries, institutions, journals, co-cited authors, and keywords of the studies were analyzed by CiteSpace software.
ResultsA total of 1088 papers were eventually included, and they were mostly from China, the United States, and India. A total of 39 papers were published by Sun Yat-sen University, the research institution with the highest number of publications. Radiology Nuclear Medicine Medical Imaging was the journal with the highest number of publications. A total of 25 high-frequency keywords and 15 burst keywords were obtained. The formation of 12 author co-citation clusters such as image segmentation and lung nodule, as well as 11 keyword clusters such as automatic segmentation and breast cancer, was observed.
ConclusionCurrent research on convolutional neural networks for oncology imaging diagnosis focuses on oncology segmentation, lung-nodule recognition, assisted diagnosis of breast cancer, and other high-frequency oncology.
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Key words:
- Convolutional neural networks /
- Oncology /
- Imaging /
- Diagnosis /
- Bibliometrics
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Competing interests: The authors declare that they have no competing interests.利益冲突声明：所有作者均声明不存在利益冲突。作者贡献：刘灵涛：设计研究方案、实施研究、撰写文章刘玉文：指导分析数据黄锦泉：实施研究、数据检索张 楚：分析数据、解释数据陈兴智：指导研究方案及撰写文章
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