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全球放射组学研究的趋势与热点:一项文献计量分析

Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis.

作者信息

Zhang Minghui, Wang Yan, Lv Mutian, Sang Li, Wang Xuemei, Yu Zijun, Yang Ziyi, Wang Zhongqing, Sang Liang

机构信息

Department of Ultrasound, The First Hospital of China Medical University, Shenyang, P. R. China.

Department of Nuclear Medicine, The First Hospital of China Medical University, Shenyang, P. R. China.

出版信息

Technol Cancer Res Treat. 2024 Jan-Dec;23:15330338241235769. doi: 10.1177/15330338241235769.

DOI:10.1177/15330338241235769
PMID:38465611
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10929060/
Abstract

The purpose of this research is to summarize the structure of radiomics-based knowledge and to explore potential trends and priorities by using bibliometric analysis. Select radiomics-related publications from 2012 to October 2022 from the Science Core Collection Web site. Use VOSviewer (version 1.6.18), CiteSpace (version 6.1.3), Tableau (version 2022), Microsoft Excel and Rstudio's free online platforms (http://bibliometric.com) for co-writing, co-citing, and co-occurrence analysis of countries, institutions, authors, references, and keywords in the field. The visual analysis is also carried out on it. The study included 6428 articles. Since 2012, there has been an increase in research papers based on radiomics. Judging by publications, China has made the largest contribution in this area. We identify the most productive institutions and authors as Fudan University and Tianjie. The top three magazines with the most publications are《FRONTIERS IN ONCOLOGY》, 《EUROPEAN RADIOLOGY》, and 《CANCERS》. According to the results of reference and keyword analysis, "deep learning, nomogram, ultrasound, f-18-fdg, machine learning, covid-19, radiogenomics" has been determined as the main research direction in the future. Radiomics is in a phase of vigorous development with broad prospects. Cross-border cooperation between countries and institutions should be strengthened in the future. It can be predicted that the development of deep learning-based models and multimodal fusion models will be the focus of future research. This study explores the current state of research and hot spots in the field of radiomics from multiple perspectives, comprehensively, and objectively reflecting the evolving trends in imaging-related research and providing a reference for future research.

摘要

本研究的目的是总结基于放射组学的知识结构,并通过文献计量分析探索潜在趋势和优先事项。从科学核心合集网站上选取2012年至2022年10月与放射组学相关的出版物。使用VOSviewer(1.6.18版本)、CiteSpace(6.1.3版本)、Tableau(2022版本)、Microsoft Excel以及Rstudio的免费在线平台(http://bibliometric.com)对该领域的国家、机构、作者、参考文献和关键词进行合著、共被引和共现分析。并对其进行可视化分析。该研究共纳入6428篇文章。自2012年以来,基于放射组学的研究论文数量有所增加。从出版物来看,中国在这一领域的贡献最大。我们确定产出最多的机构和作者分别是复旦大学和天济。发表文章最多的前三本杂志是《肿瘤前沿》《欧洲放射学》和《癌症》。根据参考文献和关键词分析结果,“深度学习、列线图、超声、F-18-FDG、机器学习、COVID-19、放射基因组学”已被确定为未来的主要研究方向。放射组学正处于蓬勃发展阶段,前景广阔。未来应加强国家和机构之间的跨境合作。可以预测,基于深度学习的模型和多模态融合模型的发展将是未来研究的重点。本研究从多个角度全面、客观地探索了放射组学领域的研究现状和热点,反映了影像相关研究的发展趋势,为未来研究提供了参考。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/9f8d0283a2d7/10.1177_15330338241235769-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/e8e895154b17/10.1177_15330338241235769-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/c4f990d10b4f/10.1177_15330338241235769-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/38dce79171e1/10.1177_15330338241235769-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/5b400803eba9/10.1177_15330338241235769-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/fdad3e7366a2/10.1177_15330338241235769-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/9f8d0283a2d7/10.1177_15330338241235769-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/e8e895154b17/10.1177_15330338241235769-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/c4f990d10b4f/10.1177_15330338241235769-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/38dce79171e1/10.1177_15330338241235769-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/5b400803eba9/10.1177_15330338241235769-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/fdad3e7366a2/10.1177_15330338241235769-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/229c/10929060/9f8d0283a2d7/10.1177_15330338241235769-fig6.jpg

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