Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years

Wang, Kangtao; Herr, Ingrid

doi:10.3389/fonc.2022.832385

Cited by 16 publications

(12 citation statements)

References 41 publications

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“…These results reflected that the United States had made great contributions and established its leading position in the field of NDDS for PC. In addition to the research topic of NDDS for PC, similar results were also obtained in the bibliometric analysis of other topics such as “pancreatic cancer” ( Wang and Herr, 2022 ), “pancreatic stellate cells” ( Yang et al, 2022b ), and “inhalable nanosystems” ( Huang et al, 2021 ). As for the cooperation among countries, it could be seen from Figure 3C that among the 20 countries with the most publications, the United States and China had the closest cooperation with other countries/regions; The United States, European countries (e.g., Italy and France), and Asian countries (e.g., China, India, and Saudi Arabia) were still the most important parts of the national cooperation landscape, and extensive cooperation had been established between them, but the cooperation of other developing countries was still relatively weak, and these countries needed to further cooperate to promote the development of NDDS for PC research field worldwide.…”

Section: Discussionsupporting

confidence: 62%

Nano-drug delivery system for pancreatic cancer: A visualization and bibliometric analysis

et al. 2022

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Background: Nano drug delivery system (NDDS) can significantly improve the delivery and efficacy of drugs against pancreatic cancer (PC) in many ways. The purpose of this study is to explore the related research fields of NDDS for PC from the perspective of bibliometrics.Methods: Articles and reviews on NDDS for PC published between 2003 and 2022 were obtained from the Web of Science Core Collection. CiteSpace, VOSviewer, R-bibliometrix, and Microsoft Excel were comprehensively used for bibliometric and visual analysis.Results: A total of 1329 papers on NDDS for PC were included. The number of papers showed an upward trend over the past 20 years. The United States contributed the most papers, followed by China, and India. Also, the United States had the highest number of total citations and H-index. The institution with the most papers was Chinese Acad Sci, which was also the most important in international institutional cooperation. Professors Couvreur P and Kazuoka K made great achievements in this field. JOURNAL OF CONTROLLED RELEASE published the most papers and was cited the most. The topics related to the tumor microenvironment such as “tumor microenvironment”, “tumor penetration”, “hypoxia”, “exosome”, and “autophagy”, PC treatment-related topics such as “immunotherapy”, “combination therapy”, “alternating magnetic field/magnetic hyperthermia”, and “ultrasound”, and gene therapy dominated by “siRNA” and “miRNA” were the research hotspots in the field of NDDS for PC.Conclusion: This study systematically uncovered a holistic picture of the performance of NDDS for PC-related literature over the past 20 years. We provided scholars to understand key information in this field with the perspective of bibliometrics, which we believe may greatly facilitate future research in this field.

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Section: Discussionsupporting

confidence: 62%

Nano-drug delivery system for pancreatic cancer: A visualization and bibliometric analysis

et al. 2022

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“…As far as countries for publication of papers are considered, a bibliometric analysis of autophagy showed that China and the United States were the most productive countries (27). Again, one bibliometric study on mitophagy (34) and the other bibliometric study on pancreatic cancer research (35) arrived the same conclusion. Our results also showed that China and the United States were the most frequent publishers in the field of autophagy and pancreatic cancer.…”

Section: Discussionmentioning

confidence: 85%

“…Previous bibliometric studies has focused on the research of autophagy ( 27 ), mitophagy ( 34 ), pancreatic cancer ( 35 ), tumor microenvironment of pancreatic cancer ( 36 ), and pancreatic neuroendocrine tumors ( 37 ). As a novel perspective, we conducted a bibliometric analysis based on literature related to autophagy of pancreatic cancer from 2011 to 2022.…”

Section: Introductionmentioning

confidence: 99%

The global research and emerging trends in autophagy of pancreatic cancer: A bibliometric and visualized study

Song

et al. 2022

Front. Oncol.

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ObjectiveTo present the global research features and hotspots, and forecast the emerging trends by conducting a bibliometric analysis based on literature related to autophagy of pancreatic cancer from 2011 to 2022.MethodsThe literature data regarding autophagy of pancreatic cancer were retrieved and downloaded from the Web of Science Core Collection (WOSCC) from Clarivate Analytics on June 10th, 2022. VOSviewer (version 1.6.18) was used to perform the bibliometric analysis.ResultsA total of 616 studies written by 3993 authors, covered 45 countries and 871 organizations, published in 263 journals and co-cited 28152 references from 2719 journals. China (n=260, 42.2%) and the United States (n=211, 34.3%) were the most frequent publishers and collaborated closely. However, publications from China had a low average number of citations (25.35 times per paper). The output of University of Texas MD Anderson Cancer Center ranked the first with 26 papers (accounting for 4.2% of the total publications). Cancers (n=23, 3.7%; Impact Factor = 6.639) published most papers in this field and was very pleasure to accept related researches. Daolin Tang and Rui Kang published the most papers (n=18, respectively). The research hotspots mainly focused on the mechanisms of autophagy in tumor onset and progression, the role of autophagy in tumor apoptosis, and autophagy-related drugs in treating pancreatic cancer (especially combined therapy). The emerging topics were chemotherapy resistance mediated by autophagy, tumor microenvironment related to autophagy, autophagy-depended epithelial-mesenchymal transition (EMT), mitophagy, and the role of autophagy in tumor invasion.ConclusionAttention has been increasing in autophagy of pancreatic cancer over the past 12 years. Our results undoubtedly provide scholars with new clues and ideas in this field.

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“…Our methodology incorporates a computer-aided text analysis within the framework of bibliometric analysis, a technique increasingly favored across various disciplines, particularly in medicine, because of its ability to interpret semantic meaning [23]. Key to our analysis was text mining and unsupervised machine learning topic modeling facilitated by a Python algorithm, methods known to offer critical insights into existing studies and future research directions [24].…”

Section: Overviewmentioning

confidence: 99%

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

Shi,

Bendig,

Vollmar

et al. 2023

J Med Internet Res

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Background Artificial intelligence (AI), conceived in the 1950s, has permeated numerous industries, intensifying in tandem with advancements in computing power. Despite the widespread adoption of AI, its integration into medicine trails other sectors. However, medical AI research has experienced substantial growth, attracting considerable attention from researchers and practitioners. Objective In the absence of an existing framework, this study aims to outline the current landscape of medical AI research and provide insights into its future developments by examining all AI-related studies within PubMed over the past 2 decades. We also propose potential data acquisition and analysis methods, developed using Python (version 3.11) and to be executed in Spyder IDE (version 5.4.3), for future analogous research. Methods Our dual-pronged approach involved (1) retrieving publication metadata related to AI from PubMed (spanning 2000-2022) via Python, including titles, abstracts, authors, journals, country, and publishing years, followed by keyword frequency analysis and (2) classifying relevant topics using latent Dirichlet allocation, an unsupervised machine learning approach, and defining the research scope of AI in medicine. In the absence of a universal medical AI taxonomy, we used an AI dictionary based on the European Commission Joint Research Centre AI Watch report, which emphasizes 8 domains: reasoning, planning, learning, perception, communication, integration and interaction, service, and AI ethics and philosophy. Results From 2000 to 2022, a comprehensive analysis of 307,701 AI-related publications from PubMed highlighted a 36-fold increase. The United States emerged as a clear frontrunner, producing 68,502 of these articles. Despite its substantial contribution in terms of volume, China lagged in terms of citation impact. Diving into specific AI domains, as the Joint Research Centre AI Watch report categorized, the learning domain emerged dominant. Our classification analysis meticulously traced the nuanced research trajectories across each domain, revealing the multifaceted and evolving nature of AI’s application in the realm of medicine. Conclusions The research topics have evolved as the volume of AI studies increases annually. Machine learning remains central to medical AI research, with deep learning expected to maintain its fundamental role. Empowered by predictive algorithms, pattern recognition, and imaging analysis capabilities, the future of AI research in medicine is anticipated to concentrate on medical diagnosis, robotic intervention, and disease management. Our topic modeling outcomes provide a clear insight into the focus of AI research in medicine over the past decades and lay the groundwork for predicting future directions. The domains that have attracted considerable research attention, primarily the learning domain, will continue to shape the trajectory of AI in medicine. Given the observed growing interest, the domain of AI ethics and philosophy also stands out as a prospective area of increased focus.

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Machine-Learning-Based Bibliometric Analysis of Pancreatic Cancer Research Over the Past 25 Years

Cited by 16 publications

References 41 publications

Nano-drug delivery system for pancreatic cancer: A visualization and bibliometric analysis

Nano-drug delivery system for pancreatic cancer: A visualization and bibliometric analysis

The global research and emerging trends in autophagy of pancreatic cancer: A bibliometric and visualized study

Mapping the Bibliometrics Landscape of AI in Medicine: Methodological Study

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