Trends and Hotspots in Global Radiomics Research: A Bibliometric Analysis

被引:1
|
作者
Zhang, Minghui [1 ]
Wang, Yan [1 ]
Lv, Mutian [2 ]
Sang, Li [3 ]
Wang, Xuemei [1 ]
Yu, Zijun [1 ]
Yang, Ziyi [1 ]
Wang, Zhongqing [4 ,6 ]
Sang, Liang [1 ,5 ]
机构
[1] China Med Univ, Dept Ultrasound, Hosp 1, Shenyang, Peoples R China
[2] China Med Univ, Dept Nucl Med, Hosp 1, Shenyang, Peoples R China
[3] Shouguang Hosp Tradit Chinese Med, Dept Acupuncture & Massage, Weifang, Peoples R China
[4] China Med Univ, Dept Informat Ctr, Hosp 1, Shenyang, Peoples R China
[5] China Med Univ, Dept Ultrasound, Hosp 1, 155 Nanjing North Rd, Shenyang 110001, Peoples R China
[6] China Med Univ, Dept Informat Ctr, Hosp1, 155 Nanjing Bei St, Shenyang 110001, Liaoning, Peoples R China
关键词
radiomics; bibliometric; artificial intelligence; machine learning; METASTASIS; TUMOR;
D O I
10.1177/15330338241235769
中图分类号
R73 [肿瘤学];
学科分类号
100214 ;
摘要
Objectives: 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. Methods: 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. Results: 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 areFRONTIERS 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. Conclusion: 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. Advances in knowledge: 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.
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页数:12
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