文献检索文档翻译深度研究
Suppr Zotero 插件Zotero 插件
邀请有礼套餐&价格历史记录

新学期,新优惠

限时优惠:9月1日-9月22日

30天高级会员仅需29元

1天体验卡首发特惠仅需5.99元

了解详情
不再提醒
插件&应用
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
高级版
套餐订阅购买积分包
AI 工具
文献检索文档翻译深度研究
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2025

Frontiers and hotspots of F-FDG PET/CT radiomics: A bibliometric analysis of the published literature.

作者信息

Liu Xinghai, Hu Xianwen, Yu Xiao, Li Pujiao, Gu Cheng, Liu Guosheng, Wu Yan, Li Dandan, Wang Pan, Cai Jiong

机构信息

Department of Nuclear Medicine, Affiliated Hospital of Zunyi Medical University, Zunyi, China.

The First Clinical College, Zunyi Medical University, Zunyi, China.

出版信息

Front Oncol. 2022 Sep 13;12:965773. doi: 10.3389/fonc.2022.965773. eCollection 2022.


DOI:10.3389/fonc.2022.965773
PMID:36176388
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9513237/
Abstract

OBJECTIVE: To illustrate the knowledge hotspots and cutting-edge research trends of F-FDG PET/CT radiomics, the knowledge structure of was systematically explored and the visualization map was analyzed. METHODS: Studies related to F-FDG PET/CT radiomics from 2013 to 2021 were identified and selected from the Web of Science Core Collection (WoSCC) using retrieval formula based on an interview. Bibliometric methods are mainly performed by CiteSpace 5.8.R3, which we use to build knowledge structures including publications, collaborative and co-cited studies, burst analysis, and so on. The performance and relevance of countries, institutions, authors, and journals were measured by knowledge maps. The research foci were analyzed through research of keywords, as well as literature co-citation analysis. Predicting trends of F-FDG PET/CT radiomics in this field utilizes a citation burst detection method. RESULTS: Through a systematic literature search, 457 articles, which were mainly published in the United States (120 articles) and China (83 articles), were finally included in this study for analysis. Memorial Sloan-Kettering Cancer Center and Southern Medical University are the most productive institutions, both with a frequency of 17. F-FDG PET/CT radiomics-related literature was frequently published with high citation in (IF9.236, 2020), (IF6.244, 2020), and (IF6.639, 2020). Further cluster profile of keywords and literature revealed that the research hotspots were primarily concentrated in the fields of image, textural feature, and positron emission tomography, and the hot research disease is a malignant tumor. Document co-citation analysis suggested that many scholars have a co-citation relationship in studies related to imaging biomarkers, texture analysis, and immunotherapy simultaneously. Burst detection suggests that adenocarcinoma studies are frontiers in F-FDG PET/CT radiomics, and the landmark literature put emphasis on the reproducibility of F-FDG PET/CT radiomics features. CONCLUSION: First, this bibliometric study provides a new perspective on F-FDG PET/CT radiomics research, especially for clinicians and researchers providing scientific quantitative analysis to measure the performance and correlation of countries, institutions, authors, and journals. Above all, there will be a continuing growth in the number of publications and citations in the field of F-FDG PET/CT. Second, the international research frontiers lie in applying F-FDG PET/CT radiomics to oncology research. Furthermore, new insights for researchers in future studies will be adenocarcinoma-related analyses. Moreover, our findings also offer suggestions for scholars to give attention to maintaining the reproducibility of F-FDG PET/CT radiomics features.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/e203437fb3b5/fonc-12-965773-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/aa97447fc9ee/fonc-12-965773-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/9b5c3832764f/fonc-12-965773-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/8d101a99b57c/fonc-12-965773-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/46dfcabfd571/fonc-12-965773-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/7e39e71f78e4/fonc-12-965773-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/7f4f24cead60/fonc-12-965773-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/941d45047d4a/fonc-12-965773-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/8cf4ba7f3e58/fonc-12-965773-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/e203437fb3b5/fonc-12-965773-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/aa97447fc9ee/fonc-12-965773-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/9b5c3832764f/fonc-12-965773-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/8d101a99b57c/fonc-12-965773-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/46dfcabfd571/fonc-12-965773-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/7e39e71f78e4/fonc-12-965773-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/7f4f24cead60/fonc-12-965773-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/941d45047d4a/fonc-12-965773-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/8cf4ba7f3e58/fonc-12-965773-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3fef/9513237/e203437fb3b5/fonc-12-965773-g009.jpg

相似文献

[1]
Frontiers and hotspots of F-FDG PET/CT radiomics: A bibliometric analysis of the published literature.

Front Oncol. 2022-9-13

[2]
Research Trends in the Application of Artificial Intelligence in Oncology: A Bibliometric and Network Visualization Study.

Front Biosci (Landmark Ed). 2022-8-31

[3]
Radiomics in Oncology: A 10-Year Bibliometric Analysis.

Front Oncol. 2021-9-20

[4]
Research on ultrasound-based radiomics: a bibliometric analysis.

Quant Imaging Med Surg. 2024-7-1

[5]
[F]sodium fluoride positron emission tomography: a systematic bibliometric review from 2008 to 2022.

Quant Imaging Med Surg. 2023-10-1

[6]
Research on application of radiomics in glioma: a bibliometric and visual analysis.

Front Oncol. 2023-9-12

[7]
Knowledge Structure and Emerging Trends of Telerehabilitation in Recent 20 Years: A Bibliometric Analysis CiteSpace.

Front Public Health. 2022

[8]
Bibliometric and visual analysis of transcranial direct current stimulation in the web of science database from 2000 to 2022 CiteSpace.

Front Hum Neurosci. 2022-12-1

[9]
Knowledge domain, research hotspots and frontiers in physiology teaching reforms from 2012 to 2021: A bibliometric and knowledge-map analysis.

Front Med (Lausanne). 2023-3-20

[10]
Knowledge domain and hotspots concerning photosensitive hydrogels for tissue engineering applications: A bibliometric and visualized analysis (1996-2022).

Front Bioeng Biotechnol. 2022-11-14

引用本文的文献

[1]
Cancer immunotherapy nursing care from 2004 to 2023: a bibliometric analysis.

Front Oncol. 2025-6-16

[2]
Mapping the research landscape of PET/CT in lymphoma: insights from a bibliometric analysis.

Front Oncol. 2025-4-8

[3]
Research trends of artificial intelligence and radiomics in lung cancer: a bibliometric analysis.

Quant Imaging Med Surg. 2024-12-5

[4]
Mapping the evolution of PET/MR research: a bibliometric analysis of publication trends, leading contributors, and conceptual frameworks (2011-2023).

EJNMMI Rep. 2024-11-1

[5]
Hotspots and trends of risk factors in gastric cancer: A visualization and bibliometric analysis.

World J Gastrointest Oncol. 2024-5-15

[6]
Validation of the 2018 FIGO staging system for stage IIIC cervical cancer by determining the metabolic and radiomic heterogeneity of primary tumors based on F-FDG PET/CT.

Abdom Radiol (NY). 2024-6

[7]
Top 100 Most-Cited Papers in Herpes Zoster from 2000 to 2022: A Bibliometric Study.

J Pain Res. 2023-5-29

本文引用的文献

[1]
MRI radiomics in overall survival prediction of local advanced cervical cancer patients tread by adjuvant chemotherapy following concurrent chemoradiotherapy or concurrent chemoradiotherapy alone.

Magn Reson Imaging. 2022-9

[2]
Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with F-FDG PET Radiomics Based Machine Learning Classification.

Diagnostics (Basel). 2022-4-24

[3]
FDG PET/CT radiomics as a tool to differentiate between reactive axillary lymphadenopathy following COVID-19 vaccination and metastatic breast cancer axillary lymphadenopathy: a pilot study.

Eur Radiol. 2022-9

[4]
Machine learning with magnetic resonance imaging for prediction of response to neoadjuvant chemotherapy in breast cancer: A systematic review and meta-analysis.

Eur J Radiol. 2022-5

[5]
A radiomics-boosted deep-learning model for COVID-19 and non-COVID-19 pneumonia classification using chest x-ray images.

Med Phys. 2022-5

[6]
Development of High-Resolution Dedicated PET-Based Radiomics Machine Learning Model to Predict Axillary Lymph Node Status in Early-Stage Breast Cancer.

Cancers (Basel). 2022-2-14

[7]
Application of Machine Learning Methods to Improve the Performance of Ultrasound in Head and Neck Oncology: A Literature Review.

Cancers (Basel). 2022-1-28

[8]
Magnetic Resonance Imaging-Based Radiomics for the Prediction of Progression-Free Survival in Patients with Nasopharyngeal Carcinoma: A Systematic Review and Meta-Analysis.

Cancers (Basel). 2022-1-27

[9]
Bibliometric Analysis of Functional Magnetic Resonance Imaging Studies on Acupuncture Analgesia Over the Past 20 Years.

J Pain Res. 2021-12-10

[10]
Radiomics in Oncology: A 10-Year Bibliometric Analysis.

Front Oncol. 2021-9-20

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

推荐工具

医学文档翻译智能文献检索