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Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases
BACKGROUND: The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction re...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288367/ https://www.ncbi.nlm.nih.gov/pubmed/37360350 http://dx.doi.org/10.3389/fneur.2023.1171167 |
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author | Cui, Jiangli Miao, Xingyu Yanghao, Xiaoyu Qin, Xuqiu |
author_facet | Cui, Jiangli Miao, Xingyu Yanghao, Xiaoyu Qin, Xuqiu |
author_sort | Cui, Jiangli |
collection | PubMed |
description | BACKGROUND: The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. METHODS: Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. RESULTS: On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. “Glioma” is a current attractive research hotspot. Keywords such as “machine learning,” “brain metastasis,” and “gene mutations” have recently appeared at the research frontier. CONCLUSION: Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors. |
format | Online Article Text |
id | pubmed-10288367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102883672023-06-24 Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases Cui, Jiangli Miao, Xingyu Yanghao, Xiaoyu Qin, Xuqiu Front Neurol Neurology BACKGROUND: The growing interest suggests that the widespread application of radiomics has facilitated the development of neurological disease diagnosis, prognosis, and classification. The application of artificial intelligence methods in radiomics has increasingly achieved outstanding prediction results in recent years. However, there are few studies that have systematically analyzed this field through bibliometrics. Our destination is to study the visual relationships of publications to identify the trends and hotspots in radiomics research and encourage more researchers to participate in radiomics studies. METHODS: Publications in radiomics in the field of neurological disease research can be retrieved from the Web of Science Core Collection. Analysis of relevant countries, institutions, journals, authors, keywords, and references is conducted using Microsoft Excel 2019, VOSviewer, and CiteSpace V. We analyze the research status and hot trends through burst detection. RESULTS: On October 23, 2022, 746 records of studies on the application of radiomics in the diagnosis of neurological disorders were retrieved and published from 2011 to 2023. Approximately half of them were written by scholars in the United States, and most were published in Frontiers in Oncology, European Radiology, Cancer, and SCIENTIFIC REPORTS. Although China ranks first in the number of publications, the United States is the driving force in the field and enjoys a good academic reputation. NORBERT GALLDIKS and JIE TIAN published the most relevant articles, while GILLIES RJ was cited the most. RADIOLOGY is a representative and influential journal in the field. “Glioma” is a current attractive research hotspot. Keywords such as “machine learning,” “brain metastasis,” and “gene mutations” have recently appeared at the research frontier. CONCLUSION: Most of the studies focus on clinical trial outcomes, such as the diagnosis, prediction, and prognosis of neurological disorders. The radiomics biomarkers and multi-omics studies of neurological disorders may soon become a hot topic and should be closely monitored, particularly the relationship between tumor-related non-invasive imaging biomarkers and the intrinsic micro-environment of tumors. Frontiers Media S.A. 2023-06-09 /pmc/articles/PMC10288367/ /pubmed/37360350 http://dx.doi.org/10.3389/fneur.2023.1171167 Text en Copyright © 2023 Cui, Miao, Yanghao and Qin. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Cui, Jiangli Miao, Xingyu Yanghao, Xiaoyu Qin, Xuqiu Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases |
title | Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases |
title_full | Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases |
title_fullStr | Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases |
title_full_unstemmed | Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases |
title_short | Bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases |
title_sort | bibliometric research on the developments of artificial intelligence in radiomics toward nervous system diseases |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10288367/ https://www.ncbi.nlm.nih.gov/pubmed/37360350 http://dx.doi.org/10.3389/fneur.2023.1171167 |
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