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. 2022 Mar 1:12:843735.
doi: 10.3389/fonc.2022.843735. eCollection 2022.

The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis

Zefeng Shen  1 , Haiyang Wu  2 , Zeshi Chen  1 , Jintao Hu  1 , Jiexin Pan  1 , Jianqiu Kong  1   3 , Tianxin Lin  1   3
Affiliations

The Global Research of Artificial Intelligence on Prostate Cancer: A 22-Year Bibliometric Analysis

Zefeng Shen et al. Front Oncol. .

Abstract

Background: With the rapid development of technology, artificial intelligence (AI) has been widely used in the diagnosis and prognosis prediction of a variety of diseases, including prostate cancer. Facts have proved that AI has broad prospects in the accurate diagnosis and treatment of prostate cancer.

Objective: This study mainly summarizes the research on the application of artificial intelligence in the field of prostate cancer through bibliometric analysis and explores possible future research hotspots.

Methods: The articles and reviews regarding application of AI in prostate cancer between 1999 and 2020 were selected from Web of Science Core Collection on August 23, 2021. Microsoft Excel 2019 and GraphPad Prism 8 were applied to analyze the targeted variables. VOSviewer (version 1.6.16), Citespace (version 5.8.R2), and a widely used online bibliometric platform were used to conduct co-authorship, co-citation, and co-occurrence analysis of countries, institutions, authors, references, and keywords in this field.

Results: A total of 2,749 articles were selected in this study. AI-related research on prostate cancer increased exponentially in recent years, of which the USA was the most productive country with 1,342 publications, and had close cooperation with many countries. The most productive institution and researcher were the Henry Ford Health System and Tewari. However, the cooperation among most institutions or researchers was not close even if the high research outputs. The result of keyword analysis could divide all studies into three clusters: "Diagnosis and Prediction AI-related study", "Non-surgery AI-related study", and "Surgery AI-related study". Meanwhile, the current research hotspots were "deep learning" and "multiparametric MRI".

Conclusions: Artificial intelligence has broad application prospects in prostate cancer, and a growing number of scholars are devoted to AI-related research on prostate cancer. Meanwhile, the cooperation among various countries and institutions needs to be strengthened in the future. It can be projected that noninvasive diagnosis and accurate minimally invasive treatment through deep learning technology will still be the research focus in the next few years.

Keywords: Citespace; VOSviewer; artificial intelligence; bibliometric; prostate cancer.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

Figure 1
Figure 1
Flowchart of the searching stagey in the study.
Figure 2
Figure 2
Global trend of publications and total citations on AI research in prostate cancer from 1999 to 2020.
Figure 3
Figure 3
(A) World map based on the total publications of different countries/regions. (B) The changing trend of the annual publication quantity in the top 10 countries/regions from 1999 to 2020. (C) The international collaborations’ visualization map of countries/regions. The thickness of the line between countries reflects the frequency of the cooperation. (D) The countries/regions’ citation network visualization map generated by using VOSviewer. The thickness of the lines reflected the citation strength.
Figure 4
Figure 4
(A) The citation network visualization map of institutions was performed with VOSviewer. (B) The institutional cooperation map created with CiteSpace. The size of node represents the publication counts of an institution, and lines between nodes represent the strength of collaborations.
Figure 5
Figure 5
The visualization map of co-authorship (A) and co-citation (B) analyses of authors carried on CiteSpace.
Figure 6
Figure 6
A dual-map overlap of journals on AI research in prostate cancer carried out by Citespace.
Figure 7
Figure 7
Citespace visualization map of Cluster view (A) and timeline view (B) of co-citation references. The time evolution is indicated with different colored lines, and the nodes on the lines indicate the references cited.
Figure 8
Figure 8
CiteSpace visualization map of top 25 references with the strongest citation bursts from 1999 to 2020.
Figure 9
Figure 9
The network visualization map of the 98 keywords with a frequency of no less than 10 times generated by using VOSviewer. (A) All the keywords could be clustered into 3 clusters: #Cluster 1 (Diagnosis and Prediction AI-related study, red nodes), #Cluster 2 (Non-Surgery AI-related study, green nodes), and #Cluster 3 (Surgery AI-related study, blue nodes). (B) The overlay visualization map of keywords. The purple and blue nodes represent the keywords appearing earlier than the green and yellow nodes.
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References

    1. Yu KH, Beam AL, Kohane IS. Artificial Intelligence in Healthcare. Nat Biomed Eng (2018) 2(10):719–31. doi: 10.1038/s41551-018-0305-z - DOI - PubMed
    1. Shin HC, Roth HR, Gao M, Lu L, Xu Z, Nogues I, et al. . Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Trans Med Imaging (2016) 35(5):1285–98. doi: 10.1109/tmi.2016.2528162 - DOI - PMC - PubMed
    1. Zhang X, Zou J, He K, Sun J. Accelerating Very Deep Convolutional Networks for Classification and Detection. IEEE Trans Pattern Anal Mach Intell (2016) 38(10):1943–55. doi: 10.1109/tpami.2015.2502579 - DOI - PubMed
    1. Miller RA. Medical Diagnostic Decision Support Systems–Past, Present, and Future: A Threaded Bibliography and Brief Commentary. J Am Med Inform Assoc (1994) 1:8–27. doi: 10.1136/jamia.1994.95236141 - DOI - PMC - PubMed
    1. Hamet P, Tremblay J. Artificial Intelligence in Medicine. Metab: Clin Exp (2017) 69s:S36–s40. doi: 10.1016/j.metabol.2017.01.011 - DOI - PubMed

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