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

作者信息

Chen Chunbao, Du Xue, Yang Lu, Liu Hongjun, Li Zhou, Gou Zhangyang, Qi Jian

机构信息

Department of Neurosurgery, Afiliated Hospital of North Sichuan Medical College, Nanchong, China.

Department of Oncology, The People's Hospital of Hechuan, Chongqing, China.

出版信息

Front Oncol. 2023 Sep 12;13:1083080. doi: 10.3389/fonc.2023.1083080. eCollection 2023.


DOI:10.3389/fonc.2023.1083080
PMID:37771434
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC10523166/
Abstract

BACKGROUND: With the continuous development of medical imaging informatics technology, radiomics has become a new and evolving field in medical applications. Radiomics aims to be an aid to support clinical decision making by extracting quantitative features from medical images and has a very wide range of applications. The purpose of this study was to perform a bibliometric and visual analysis of scientific results and research trends in the research application of radiomics in glioma. METHODS: We searched the Web of Science Core Collection (WOScc) for publications related to glioma radiomics. A bibliometric and visual analysis of online publications in this field related to countries/regions, authors, journals, references and keywords was performed using CiteSpace and R software. RESULTS: A total of 587 relevant literature published from 2012 to September 2022 were retrieved in WOScc, and finally a total of 484 publications were obtained according to the filtering criteria, including 393 (81.20%) articles and 91 (18.80%) reviews. The number of relevant publications increases year by year. The highest number of publications was from the USA (171 articles, 35.33%) and China (170 articles, 35.12%). The research institution with the highest number of publications was Chinese Acad Sci (24), followed by Univ Penn (22) and Fudan Univ (21). WANG Y (27) had the most publications, followed by LI Y (22), and WANG J (20). Among the 555 co-cited authors, LOUIS DN (207) and KICKINGEREDER P (207) were the most cited authors. FRONTIERS IN ONCOLOGY (42) was the most published journal and NEURO-ONCOLOGY (412) was the most co-cited journal. The most frequent keywords in all publications included glioblastoma (187), survival (136), classification (131), magnetic resonance imaging (113), machine learning (100), tumor (82), and feature (79), central nervous system (66), IDH (57), and radiomics (55). Cluster analysis was performed on the basis of keyword co-occurrence, and a total of 16 clusters were formed, indicating that these directions are the current hotspots of radiomics research applications in glioma and may be the future directions of continuous development. CONCLUSION: In the past decade, radiomics has received much attention in the medical field and has been widely used in clinical research applications. Cooperation and communication between countries/regions need to be enhanced in future research to promote the development of radiomics in the field of medicine. In addition, the application of radiomics has improved the accuracy of pre-treatment diagnosis, efficacy prediction and prognosis assessment of glioma and helped to promote the development into precision medicine, the future still faces many challenges.

摘要
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/af0729669b31/fonc-13-1083080-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/e350b4012fc6/fonc-13-1083080-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/cd438522d8f1/fonc-13-1083080-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/3c2a8b82eb54/fonc-13-1083080-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/a7b9ed9b665d/fonc-13-1083080-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/3d1ff1b6b3b6/fonc-13-1083080-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/32d06b2baa43/fonc-13-1083080-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/af0729669b31/fonc-13-1083080-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/e350b4012fc6/fonc-13-1083080-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/cd438522d8f1/fonc-13-1083080-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/3c2a8b82eb54/fonc-13-1083080-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/a7b9ed9b665d/fonc-13-1083080-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/3d1ff1b6b3b6/fonc-13-1083080-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/32d06b2baa43/fonc-13-1083080-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a0f5/10523166/af0729669b31/fonc-13-1083080-g007.jpg

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[2]
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本文引用的文献

[1]
An automated approach for predicting glioma grade and survival of LGG patients using CNN and radiomics.

Front Oncol. 2022-8-12

[2]
Overall Survival Prediction of Glioma Patients With Multiregional Radiomics.

Front Neurosci. 2022-7-7

[3]
Static FET PET radiomics for the differentiation of treatment-related changes from glioma progression.

J Neurooncol. 2022-9

[4]
Primary brain and other central nervous system tumors in the United States (2014-2018): A summary of the CBTRUS statistical report for clinicians.

Neurooncol Pract. 2022-2-22

[5]
Radiomics-Based Method for Predicting the Glioma Subtype as Defined by Tumor Grade, IDH Mutation, and 1p/19q Codeletion.

Cancers (Basel). 2022-3-31

[6]
An MRI radiomics approach to predict survival and tumour-infiltrating macrophages in gliomas.

Brain. 2022-4-29

[7]
Radiomics and Qualitative Features From Multiparametric MRI Predict Molecular Subtypes in Patients With Lower-Grade Glioma.

Front Oncol. 2022-1-21

[8]
Radiomics for precision medicine in glioblastoma.

J Neurooncol. 2022-1

[9]
Applications of Radiomics and Radiogenomics in High-Grade Gliomas in the Era of Precision Medicine.

Cancers (Basel). 2021-11-25

[10]
Radiomics signature for temporal evolution and recurrence patterns of glioblastoma using multimodal magnetic resonance imaging.

NMR Biomed. 2022-3

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