Knowledge mapping of application of image-guided surgery in prostate cancer: a bibliometric analysis (2013-2023).
Image-guided surgery (IGS) refers to surgery navigated by medical imaging technology, helping doctors better clarify tumor boundaries, identify metastatic lymph nodes and preserve surrounding healthy tissue function. Recent studies have provided expectable momentum of the application of IGS in prostate cancer (PCa). The authors aim to comprehensively construct a bibliometric analysis of the application of IGS in PCa.
The authors searched publications related to application of IGS in PCa from 2013 to 2023 on the web of science core collection (WoSCC) databases. VOSviewer, CiteSpace, and R package 'bibliometrix' were used for bibliometric analysis.
Two thousand three eighty-nine articles from 75 countries and 2883 institutions led by the United States were included. The number of publications related to the application of IGS in PCa kept high in the last decade. Johns Hopkins University is the top research institutions. Journal of Nuclear Medicine has the highest popularity as the selection of journal and co-cited journal. Pomper Martin G. had published the most paper. Ali Afshar-Oromieh was co-cited most frequently. The clinical efficacy of PSMA-PET/CT in PCa diagnosis and treatment are main topics in this research field, with emerging focuses on the use of fluorescence imaging guidance technology in PCa. 'PSMA' and 'PET/CT' are the main keywords as long-term research hotspots.
This study is the first bibliometric analysis of researches on application of IGS in PCa with three recognized bibliometric software, providing an objective description and comprehensive guidance for the future relevant investigations.
Zeng N
，Sun JX
，Liu CQ
，Xu JZ
，An Y
，Xu MY
，Zhang SH
，Zhong XY
，Ma SY
，He HD
，Wang SG
，Xia QD
... -
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Research hotspots and frontiers of machine learning in renal medicine: a bibliometric and visual analysis from 2013 to 2024.
The kidney, an essential organ of the human body, can suffer pathological damage that can potentially have serious adverse consequences on the human body and even affect life. Furthermore, the majority of kidney-induced illnesses are frequently not readily identifiable in their early stages. Once they have progressed to a more advanced stage, they impact the individual's quality of life and burden the family and broader society. In recent years, to solve this challenge well, the application of machine learning techniques in renal medicine has received much attention from researchers, and many results have been achieved in disease diagnosis and prediction. Nevertheless, studies that have conducted a comprehensive bibliometric analysis of the field have yet to be identified.
This study employs bibliometric and visualization analyses to assess the progress of the application of machine learning in the renal field and to explore research trends and hotspots in the field.
A search was conducted using the Web of Science Core Collection database, which yielded articles and review articles published from the database's inception to May 12, 2024. The data extracted from these articles and review articles were then analyzed. A bibliometric and visualization analysis was conducted using the VOSviewer, CiteSpace, and Bibliometric (R-Tool of R-Studio) software.
2,358 papers were retrieved and analyzed for this topic. From 2013 to 2024, the number of publications and the frequency of citations in the relevant research areas have exhibited a consistent and notable increase annually. The data set comprises 3734 institutions in 91 countries and territories, with 799 journals publishing the results. The total number of authors contributing to the data set is 14,396. China and the United States have the highest number of published papers, with 721 and 525 papers, respectively. Harvard University and the University of California System exert the most significant influence at the institutional level. Regarding authors, Cheungpasitporn, Wisit, and Thongprayoon Charat of the Mayo Clinic organization were the most prolific researchers, with 23 publications each. It is noteworthy that researcher Breiman I had the highest co-citation frequency. The journal with the most published papers was "Scientific Reports," while "PLoS One" had the highest co-citation frequency. In this field of machine learning applied to renal medicine, the article "A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury" by Tomasev N et al., published in NATURE in 2019, emerged as the most influential article with the highest co-citation frequency. A keyword and reference co-occurrence analysis reveals that current research trends and frontiers in nephrology are the management of patients with renal disease, prediction and diagnosis of renal disease, imaging of renal disease, and development of personalized treatment plans for patients with renal disease. "Acute kidney injury," "chronic kidney disease," and "kidney tumors" are the most discussed diseases in medical research.
The field of renal medicine is witnessing a surge in the application of machine learning. On one hand, this study offers a novel perspective on applying machine learning techniques to kidney-related diseases based on bibliometric analysis. This analysis provides a comprehensive overview of the current status and emerging research areas in the field, as well as future trends and frontiers. Conversely, this study furnishes data on collaboration and exchange between countries, regions, institutions, journals, authors, keywords, and reference co-citations. This information can facilitate the advancement of future research endeavors, which aim to enhance interdisciplinary collaboration, optimize data sharing and quality, and further advance the application of machine learning in the renal field.
Li F
，Hu C
，Luo X
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Developmental trends and knowledge frameworks in the application of radiomics in prostate cancer: a bibliometric analysis from 2000 to 2024.
This research utilized the bibliometrics method to analyze the published literature related to prostate cancer (PCa) imaging. Furthermore, current knowledge and research hotspots of radiomics in PCa diagnosis and treatment were comprehensively reviewed, as well as progress and emerging trends in field were explored.
In this investigation, the relevant literature on radiomics, and PCa was retrieved from Web of Science Core Collection (WoSCC) databases from 2000 and 2024. Furthermore, a comprehensive bibliometric analysis was carried out using advanced tools like CiteSpace6.2, VOS viewer, and the 'bibliometrix' package of R software to visualize the annual distribution of publications across various aspects such as authors, countries, journals, institutions, and keywords.
This analysis included 593 from 58 countries including China and the United States. Chinese Academy of Sciences and Frontiers in Oncology were the institutions and journals that publish the most relevant articles, -while Radiology journal had the greatest number of co-cited publications. Furthermore, 3,621 authors published on this topic, of which Madabhushi Anant and Stoyanova Radka had the highest contributions. Moreover, Lambin, P. had the most co-citations. In addition, the diagnostic characteristics of radiomics in PCa imaging and treatment strategies are the current research focal points. The establishment of multi-functional imaging techniques and independent factor models warrants future investigation.
In summary, this analysis revealed that the research on PCa imaging is developing vigorously, focusing on the diagnostic methods and intervention measures of imaging in PCa diagnosis and treatment. In the future, there is an urgent need for improved collaboration and communication among countries and institutions.
Hao P
，Xin R
，Li Y
，Na X
，Lv X
... -
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Brain-Computer Interface for Patients with Spinal Cord Injury: A Bibliometric Study.
Spinal cord injury (SCI) is a debilitating condition with profound implications on patients' quality of life. Recent advancements in brain-computer interface (BCI) technology have provided novel opportunities for individuals with paralysis due to SCI. Consequently, research on the application of BCI for treating SCI has received increasing attention from scholars worldwide. However, there is a lack of rigorous bibliometric studies on the evolution and trends in this field. Hence, the present study aimed to use bibliometric methods to investigate the current status and emerging trends in the field of applying BCI for treating SCI and thus identify novel therapeutic options for SCI.
We conducted a comprehensive review of the relevant literature on BCI applications for treating SCI published between 2005 and 2024 by using the Web of Science Core Collection database. To facilitate visualization and quantitative analysis of the published literature, we used VOSviewer and CiteSpace software tools. These tools enabled the assessment of co-authorships, co-occurrences, citations, and co-citations in the selected literature, thereby providing an overview of the current trends and predictive insights into the field.
The literature search yielded 714 publications from the Web of Science Core Collection database. The findings indicated a significant upward trend in the number of publications, yielding a total of 24,804 citations, with an average citation rate of 34.74 per publication and an H-index of 75. Research contributions were identified from 54 countries/regions, and the United States, China, and Germany emerged as the predominant contributors. A total of 1114 research institutions contributed to the retrieved literature, with Harvard Medical School, Brown University, and Northwestern University producing the highest number of publications. The published literature was predominantly distributed across 258 academic journals, and the Journal of Neural Engineering was the most frequently utilized publication source. Hochberg, Leigh, Henderson, Jaimie, and Collinger were the prominent authors in this field.
In recent years, there has been a steep increase in research on the use of BCI for treating SCI. Existing research focuses on the application of BCI for improving rehabilitation and quality of life of patients with SCI. Interdisciplinary collaboration is the current trend in this field.
Feng J
，Gao S
，Hu Y
，Sun G
，Sheng W
... -
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ResearchGate
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