Application of ultrasound in liver cancer from 2014--2024: Bibliometric analysis and global trends

Application of ultrasound in liver cancer from 2014--2024: Bibliometric analysis and global trends | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Application of ultrasound in liver cancer from 2014--2024: Bibliometric analysis and global trends Yaping Zhu, Xinju Chen, Yanyan Lu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6503008/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background: Liver cancer remains one of the most prevalent and lethal malignancies worldwide, highlighting the need for effective diagnostic and monitoring strategies. Ultrasound plays a vital role in the early diagnosis, monitoring, and treatment of liver cancer. However，no bibliometric analysis has been conducted in this field before. This study aims to provide a comprehensive overview of the knowledge structure and research hotspots related to the application of ultrasound in liver cancer via bibliometric methodologies. Methods: A search was performed in the Web of Science Core Collection database for English literature studies on the application of ultrasound in liver cancer from 2014 -- 2024. Bibliometric analysis tools including VOSviewer, CiteSpace, and R Studio, were utilized to analyze global trends and research hotspots in this field. Results: A total of 2501 eligible publications, including 2048 articles and 453 reviews, were analyzed. In the past decade, both the annual output of publications and the citation rates have rapidly increased. The majority of published articles on this topic were originated in China (n = 832, 33.27%), followed by the United States (n = 586, 23.43%), and Italy (n = 222, 8.88%). Researchers from the United States have demonstrated high productivity, prominence, and influence in this area of research. Additionally, Sun Yat-sen University published the most papers (n = 64), whereas the University of Michigan had the highest average citation value (value = 60.28) related to research on the application of ultrasound in liver cancer. Notably, Singal, Amit G from the USA was the author with both the highest number of published articles and the highest average citation value. Conclusion: In recent years, rapid advancements in ultrasound research for liver cancer have been reported. Increasing evidence has illustrated the crucial role of ultrasound in the early diagnosis and monitoring of liver cancer. Ultrasound Liver cancer Bibliometric analysis Trends Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 1. Introduction Liver cancer is a significant global health concern, and one of the leading causes of cancer-related mortality. Various risk factors contribute to the epidemiology of liver cancer, including chronic viral hepatitis, particularly hepatitis B and C infections; alcohol consumption; and nonalcoholic fatty liver disease. In many regions, the majority of patients are diagnosed at advanced stages, which severely limits treatment options and adversely affects survival rates [ 1 ]. Studies have shown that nearly two-thirds of liver cancer patients are diagnosed at an advanced stage, highlighting the urgent need for better early detection methods [ 2 ]. The clinical importance of liver cancer cannot be overstated, as its increasing incidence is projected to continue, particularly in developing countries where risk factors are prevalent [ 3 ]. Traditional diagnostic methods for liver cancer, including imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), have limitations such as high costs and radiation exposure [ 4 ]. These methods often fail to detect small or early-stage tumors, leading to delayed diagnosis [ 5 ]. Additionally, relying on serum biomarkers such as alpha-fetoprotein (AFP) can be problematic. Although useful, these markers are not definitive and may produce false positives or negatives on the basis of the condition of the liver [ 6 ]. As a result, there is increasing recognition of the need for more accessible and reliable diagnostic tools that can facilitate the early detection of liver cancer and improve patient outcomes. Ultrasound technology has evolved significantly and is now a cornerstone in the diagnosis and management of liver cancer. The basic principle of ultrasound relies on the emission of high-frequency sound waves that reflect off tissues, creating images on the basis of varying densities of the tissues encountered [ 7 ]. Over the years, advancements in ultrasound techniques, such as contrast-enhanced ultrasound (CEUS) and elastography, have enhanced its diagnostic capabilities [ 8 ]. These innovations establish ultrasound as both a diagnostic tool and a method for monitoring disease progression and treatment response. Ultrasound-guided treatment is real-time and can greatly reduce the risk of damage to various tissues, such as blood vessels and nerves [ 9 ]. Although research on the application of ultrasound in liver cancer has increased in recent years, there is still a lack of systematic analysis of global research trends in this specific field. This study aims to highlight the importance of ultrasound technology in the detection and management of liver cancer, and to provide insights for future research directions in this area. 2. Methods 2.1. Data sources and retrieval strategies We conducted a comprehensive literature search for all included studies of ultrasound and liver cancer in the Web of Science Core Collection (WoSCC, https://www.webofscience.com/ ) database. The search was independently performed and extracted by coauthors (YZ and YL) and those that did not comply with the following inclusion and exclusion criteria were excluded: (1) Publications from the WoSCC Science Citation Index Expanded (SCI-E) databases. (2) English language only, and (3) only original articles and reviews. (4) Exclusion of other publication types, such as early access, letters, retracted publications, corrections, proceeding papers, book chapters, editorial materials, meeting abstracts, and news items. (5) The retrieval time range is from January 01, 2014 to December 25, 2024. The detailed WoSCC database search strategy is outlined in Table 1 . Finally, 2501 eligible publications were identified, comprising 2048 articles and 453 reviews for subsequent analysis (Fig. 1 ). Table 1 Search strategy for the Web of Science Core Collection (WoSCC). Research database Web of Science Core Collection Citation indexes Science Citation Index Expanded Query formulation ultrasound (Topic) AND liver cancer (Topic) Language English type of articles Articles and Reviews Searching period January 01, 2014 to December 25, 2024 Data collection exported with full records and cite reference in plain text format Sample size 2501 publications, comprising 2048 articles and 453 reviews 2.2. Data analyses and visualization Mainstream bibliometric analysis tools including VOSviewer [ 10 ], CiteSpace, R software and Excel were employed for data analysis and visualization in this study. VOSviewer, developed by Leiden University in the Netherlands, is a powerful software for constructing and visualizing bibliometric networks on the basis of publications, countries, authors, journals and keywords. Burst keywords indicative of potential research hotspots were explored via CiteSpace [ 12 ] from Drexel University in Philadelphia, PA, USA. Additionally, the R package bibliometrix was employed to create a collaboration map of countries involved in ultrasound and liver cancer research. 3. Results 3.1. Annual tendencies and annual citations In accordance with the search strategy delineated in Table 1 , a total of 2728 eligible publications were identified from the WoSCC database spanning the period from January 1, 2014, to December 25, 2024. A total of 228 documents were subsequently extracted, including early access (N = 28), letters (N = 5), retracted publications (N = 10), corrections (N = 3), proceeding papers (N = 138), book chapters (N = 7), editorial materials (N = 30), and meeting abstracts (N = 7). Finally, 2501 publications were deemed eligible for further analysis, comprising 2048 research articles and 453 review papers (Fig. 1 ). Research on the use of ultrasound applications in the diagnosis and treatment of liver cancer has consistently and notably increased over the past decade (Fig. 2 a). The number of citations for these articles has grown exponentially each year, reaching a peak of 10,140 in 2024, highlighting the increasing interest in ultrasound applications in liver cancer research (Fig. 2 a). China and the United States led in the volume of published research findings (Fig. 2 b). As of December 25, 2024, the 2,501 publications were cited 52,883 times, yielding an average citation rate of 21.1 citations per publication. 3.2. Distribution of countries/regions Over the last decade, a total of 3313 research institutions from 412 countries have contributed to the literature concerning the use of ultrasound in the diagnosis and treatment of liver cancer. The articles were published in 833 journals and written by 2,501 authors. Information from the published articles was used to create a world map illustrating the cooperative relationships among the research topics in different countries (Fig. 3 ). Countries were ranked on the basis of the number of publications, with only the top ten nations displayed in Table 2 . Notably, China produced the highest volume of articles on this subject (n = 832, 33.27%), followed by the United States (n = 586, 23.43%), and Italy (n = 222, 8.88%). These three countries have made significant contributions to the application of ultrasound in liver cancer research. Although the USA ranks second in the number of publications, it boasts the highest citation score, highlighting its pivotal role as a research hub in this discipline. Conversely, while China leads in publication volume, its average citation score stands at only 15.86. Figures 4 and 5 present the collaboration maps of countries related to ultrasound and liver cancer. The results show that China largely cooperated with the USA, England, Germany, France, and Italy. This shows that scientific research crosses geographic boundaries and highlights the importance of international collaboration in advancing this field. Table 2 Top 10 productive countries related to ultrasound and liver cancer. Rank Country Documents (n) Percentage (n/1182) Citations Average citations 1 China 832 33.27% 13192 15.86 2 USA 586 23.43% 21062 35.94 3 Italy 222 8.88% 6992 31.50 4 Japan 155 6.20% 3867 24.95 5 England 135 5.40% 4156 30.79 6 France 120 4.80% 5011 41.76 7 Germany 117 4.68% 4299 36.74 8 South Korea 107 4.28% 4390 41.03 9 Canada 89 3.56% 3535 39.72 10 India 76 3.04% 1015 13.36 3.3. Distribution of institutions VOSviewer was used to analyze and visualize the 3313 research institutions that contributed to the field. Publications (the minimum number of documents used by an organization was defined as more than five) were identified in the 249 institutions and visualized via VOSviewer (Fig. 6 ). The top 12 contributing research institutions are summarized in Table 3 . There is a close cooperative relationship among these institutions. In the past 10 years, more than half of the top 10 research institutions were from China (8/12), followed by the USA (3/12). The results indicate that institutions in China and the USA have made significant contributions to the fields of ultrasound and liver cancer. Sun Yat-sen University published the most papers (n = 64), whereas the University of Michigan has the highest average citation value (value = 60.28). Given the importance of collaboration, institutions need to establish closer interagency collaboration in the future. Table 3 Top 15 institutions ranked by the number of publications. Rank Organization Country Documents Citations Average citation 1 Sun Yat-sen University China 64 1081 16.89 2 ‌Fudan University China 59 1090 18.47 3 Chongqing Medical University China 48 906 18.88 4 Chinese PLA General Hospital China 45 825 18.33 5 Zhejiang University China 42 588 14.00 6 Shanghai Jiao Tong University China 41 480 11.71 7 University of Michigan USA 40 2411 60.28 8 Mayo Clinic USA 39 1390 35.64 9 The University of Milan Italy 37 1405 37.97 10 Sichuan University China 36 836 23.22 11 Stanford University USA 34 1934 56.88 12 Huazhong University of Science and Technology China 31 637 20.55 3.4. Contributions of authors and cited authors The authors (n = 153) with a minimum productivity of 5 publications were visualized via VOSviewer software and are shown in Fig. 7a. Moreover, the collaborative relationships among these authors are displayed. The top 11 authors with the most actively published articles are also listed (Table 4 ). Among these authors, Singal, Amit G has the highest number of published articles and the highest average of citations, indicating his significant academic influence and contributions to this field. The co-citation analysis of the authors was performed via VOSviewer software (Fig. 7b). A total of 35 authors who had more than 100 citations were selected. Table 4 Top 10 authors with the most actively published articles Rank Author Country Documents Citations Average citation 1 Singal, Amit G USA 41 4047 98.71 2 Liang, Ping China 38 1939 51.03 3 Yu, Jie China 34 675 19.85 4 Parikh, Neehar D USA 21 1808 86.10 5 Cheng, Zhigang China 17 263 15.47 6 Wang, Wei China 17 379 22.29 7 Yu, Xiaoling China 17 264 15.53 8 Dong, Yi China 15 163 10.87 9 Han, Zhi-Yu China 15 233 15.53 10 Liu, Fang-Yi China 15 225 15.00 11 Wang, Zhigang China 15 503 33.53 3.5. Analysis of high-yielding journals From January 01, 2014 to December 25, 2024, a total of 2501 publications related to ultrasound and liver cancer were published in 833 journals, 119 of which contained at least 5 articles. A bibliographic coupling analysis of journals was performed, and a network map was generated (Fig. 8 ). The top 10 journals with the most publications in this field are listed in Table 5 . Among them, Cancers has the highest impact factor (IF), with a value of 4.5. Additionally, the International Journal of Hyperthermia (2023 IF: 3), Medicine (2023 IF: 1.4), and World Journal of Gastroenterology (2023 IF: 4.3) are the journals with more than 40 publications each. Furthermore, combined with the information provided by VOSviewer software and the Journal Citation Reports (JCR) assessment system, we also found that the top 10 journals are concentrated in JCR categories Q1(50%) and Q2 (50%), which indicates their substantial influence in the relevant fields. As presented in Table 5 , the World Journal of Gastroenterology yielded the most citations, with 1462, followed by Ultrasound in Medicine and Biology, with 1078 citations. The International Journal of Hyperthermia has 997 citations. Table 5 Top 10 journals with the greatest number of articles. Rank Source documents citations 2023IF JCR 1 Ultrasound In Medicine and Biology 74 1078 2.4 Q2 2 International Journal of Hyperthermia 53 997 3 Q2 3 Medicine 43 284 1.4 Q2 4 World Journal of Gastroenterology 41 1462 4.3 Q1 5 Frontiers in Oncology 40 280 3.5 Q2 6 Cancers 38 381 4.5 Q1 7 Scientific Reports 34 491 3.8 Q1 8 Cureus Journal of Medical Science 33 31 2.3 Q2 9 PLoS ONE 33 531 2.9 Q1 10 Diagnostics 31 239 3 Q1 3.6. Analysis of the number of citations The number of citations is a critical indicator of the impact of a publication in a scientific field. These 2501 publications were counted and ranked by the number of citations. The top 10 publications are listed in Table 6. The most cited article originates from a group author of the European Association for the Study of the Liver published in the Journal of Hepatology in 2018 with 4623 citations. The second most cited article was published in Gastroenterology in 2019 with 1652 citations. Additionally, the third most cited article by Sigrist, RMS was published in Theranostics in 2017 with 1131 citations. These publications demonstrate that ultrasound plays an important role in the diagnosis and treatment of liver cancer. Table 6 Top 10 most cited articles related to ultrasound and liver cancer. Article Author Years Journals 2023 IF Citations EASL Clinical Practice Guidelines: Management of hepatocellular carcinoma European Assoc Study Liver 2018 Journal of Hepatology 26.8 4623 Burden and Cost of Gastrointestinal, Liver, and Pancreatic Diseases in the United States: Update 2018 Peery, AF 2019 Gastroenterology 26.3 1652 Ultrasound Elastography: Review of Techniques and Clinical Applications Sigrist, RMS 2017 Theranostics 12.4 1131 Image-guided Tumor Ablation: Standardization of Terminology and Reporting Criteria-A 10-Year Update Ahmed, M 2014 Radiology 12.1 1025 The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review Younossi, ZM 2023 Hepatology 13 827 Surveillance Imaging and Alpha Fetoprotein for Early Detection of Hepatocellular Carcinoma in Patients with Cirrhosis: A Meta-analysis Tzartzeva, K 2018 Gastroenterology 26.3 718 Epidemiology and surveillance for hepatocellular carcinoma: New trends Singal, AG 2020 Journal of Hepatology 26.8 687 Early Detection, Curative Treatment, and Survival Rates for Hepatocellular Carcinoma Surveillance in Patients with Cirrhosis: A Meta-analysis Singal, AG 2014 PLOS Medicine 10.5 583 Risk factors and prevention of hepatocellular carcinoma in the era of precision medicine Fujiwara, N 2018 Journal of Hepatology 26.8 504 2019 Chinese clinical guidelines for the management of hepatocellular carcinoma: updates and insights Xie, DY 2020 Hepatobiliary Surgery and Nutrition 6.1 323 3.7. Analysis of keywords On the basis of the literature keyword co-occurrence analysis, a detailed description of hotspot topics in the research field is presented. By analyzing the keywords of the 2501 publications, a total of 5182 keywords were identified. VOSviewer identified 36 hotspot keywords appeared at least 30 times and visualized connections through a network map (Fig. 9 a). Table 7 lists the top 12 hotspot keywords with the highest frequencies. Analyzing keywords with the strongest citation bursts reveals that "burst words" are frequently cited keywords during specific periods, reflecting the academic frontier in this field. In the present study, CiteSpace software was used to identify keywords with strong citation bursts; 12 “Burst words” are presented. Figure 9 b displays the top 12 “Burst words” related to ultrasound and liver cancer from 2014–2024. The red strip indicates the period during which the burst keywords were maintained. Among them, “case report” was the most frequent keyword with strong citation bursts, which began in 2021 and ended in 2024, with the highest strength of 10.07. The second most frequent keyword, “surgery,” had a strength of 6.68 and was associated with the treatment of liver cancer under ultrasound guidance from 2014–2015. Hepatocellular carcinoma(HCC)has emerged as the latest strong citation burst, indicating that it may be a hot topic in recent years. Table 7 Top 12 hotspot keywords with the highest frequencies. Rank keyword occurrences total link strength 1 hepatocellular carcinoma 365 282 2 ultrasound 250 192 3 liver cancer 191 133 4 liver 121 105 5 contrast-enhanced ultrasound 111 84 6 ultrasonography 84 50 7 cirrhosis 77 104 8 cancer 72 45 9 surveillance 71 106 10 magnetic resonance imaging 65 81 11 radiofrequency ablation 65 44 12 diagnosis 63 64 4. Discussion Ultrasound has garnered considerable attention for its role in the early diagnosis of liver cancer. Its noninvasive nature, cost-effectiveness, and real-time imaging capabilities are highly advantageous. Early detection is pivotal for improving treatment outcomes and survival rates, as liver cancer often presents at advanced stages with a poor prognosis. Recent studies have shown that ultrasound, particularly when enhanced with contrast agents, can markedly increase the sensitivity and accuracy of liver cancer diagnosis. Furthermore, ultrasound-guided interventional therapy has also been increasingly recognized in liver cancer treatment. The real-time imaging provided by ultrasound allows doctors to locate lesions precisely during procedures such as puncture, injection, or ablation. Research has demonstrated that ultrasound-guided liver puncture and radiofrequency ablation can significantly increase treatment success rates and reduce complications [ 5 ]. The increasing importance of ultrasound in the early diagnosis and monitoring of liver cancer cannot be overstated. Further exploration can provide valuable insights for future research directions and clinical practice in liver cancer management. In our bibliometric analysis, we identified 2501 publications, including 2048 articles and 453 reviews related to ultrasound and liver cancer research from the WoSCC database. The number of studies and citations in this field has been steadily and exponentially increasing since 2014, indicating that increasing attention has been given to this area. Notable studies have examined various aspects of this area of research, including diagnosis, CEUS, and radiofrequency ablation [ 13 – 15 ]. We analyzed the contributions of countries, institutions, journals, and authors. China and the USA are the top contributors to academic research, with China leading in published papers and the USA leading in citations. Considering the increasing incidence of liver cancer in China, which has imposed a considerable burden on society, research output from China, including Chinese institutions and Chinese authors, is also increasing annually [ 16 ]. However, Chinese scholars’ articles have a lower average citation count than those of the other top 10 countries do, indicating a need to prioritize impactful research over mere quantity. In contrast, France has the highest average citation count, indicating its influential role in this research area. Sun Yat-sen University in China and the University of Michigan in the USA have the highest number of published papers and average citations, respectively. The publications of Sun Yat-sen University have focus mainly on "CEUS" and "ablation", whereas those of the University of Michigan have focus mainly on "screening" and "early detection". The differences in their focuses also reflect the different national conditions they face. Their pioneering research has shed new light on the application of ultrasound in liver cancer, deepening our understanding of the diagnosis and monitoring of this deadly disease. Their groundbreaking work represents a true testament to the power of scientific research and the immense potential for collaboration in tackling some of the most pressing health challenges of our time. However, there is very little collaboration between the two research institutions. Moreover, we found that most publications are concentrated in economically developed countries and high-ranking universities, which are closely linked to research resources, culture, and international collaboration opportunities. Moreover, Singal, Amit G, a renowned scholar from the USA, holds the highest number of published articles and the highest average citation record in his field. His research revealed that sub-centimeter liver lesion ultrasound recall patterns are variable. Primary liver cancer (PLC) risk support 3–6 months of ultrasound; high-risk patients may need CT/MRI. Cirrhotic patients, particularly those who are obese or have fatty liver disease, often have limited liver cancer nodule visualization via ultrasound, but the image quality can improve. Blood biomarkers show potential; however, they need validation and logistical support before they can be used clinically [ 17 – 19 ]. The scientific community greatly appreciates his outstanding contributions to the field. Unlike other top ranked keywords such as "liver cancer" and "ultrasound", CEUS, as the fifth ranked keyword, is a truly novel, advanced ultrasound technology that has received increasing attention in the past decade. By using microbubble contrast agents, CEUS can improve the visualization of liver lesions, allowing real-time imaging that can differentiate between benign and malignant lesions on the basis of their vascular characteristics [ 20 ]. A meta-analysis also indicated that CEUS boasts a combined sensitivity of 92% and specificity of 93% for early liver cancer diagnosis, suggesting its potential as a primary screening tool in high-risk populations [ 21 ]. CEUS offers several advantages over other imaging modalities, such as CT and MRI, including the absence of ionizing radiation and the ability to perform dynamic assessments of blood flow [ 22 ]. Recent advancements in CEUS technology, including the development of nanobubble agents, have further expanded its applications, allowing for improved visualization of microvascular structures and enhanced diagnostic accuracy in challenging cases [ 23 ], potentially replacing or complementing existing imaging techniques. The integration of multimodal imaging techniques represents a promising advancement in the management of liver cancer. Combining ultrasound with other imaging modalities, such as CT and MRI, can provide a more comprehensive assessment of liver lesions. Furthermore, the incorporation of artificial intelligence (AI) in multimodal imaging can enhance the interpretation of complex imaging data, leading to better differentiation between benign and malignant lesions [ 24 ]. However, effectively implementing multimodal imaging involves addressing challenges in data integration, standardizing imaging protocols, and developing strong AI algorithms. Addressing these challenges will facilitate the effective use of multimodal imaging in clinical practice, ultimately improving the management of liver cancer [ 25 , 26 ]. This study is the first to use bibliometric methods to explore the application of ultrasound in liver cancer research over the past decade. However, there are limitations. First, our publications were limited to the WOSCC database, which means that we excluded other sources such as Google Scholar, PubMed, and MEDLINE. Second, our research focused mainly on English-language literature, potentially missing high-quality literature in other languages. We hope these limitations can be addressed in future studies. 5. Conclusion In this study, we explored the application of ultrasound in the diagnosis and treatment of liver cancer via bibliometric methods. The connection between ultrasound and liver cancer is a significant area of research. Through bibliometric analysis, we found that ultrasound is an important tool for the early diagnosis and treatment of liver cancer, as evidenced by a published literature review. Declarations Funding This research was supported by the Henan Provincial Special Research Project on Traditional Chinese Medicine (grant no. 2022JDZX018), the Second Batch of Henan Provincial Characteristic and Backbone Discipline Construction Projects in Traditional Chinese Medicine (grant no. STG-ZYX01-202101), and the Henan Provincial Natural Science Foundation General Program (grant no. 242300421298). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. CRediT authorship contribution statement Yanyan Lu: Writing–review & editing, Supervision, Project administration, Methodology, Conceptualization. Xinju Chen: Writing– review & editing, Formal analysis, Data curation, Funding acquisition. Yaping Zhu: Writing–review & editing, Writing–original draft, Visualization, Formal analysis, Data curation, Conceptualization. Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Appendix A. Supporting information Supplementary data associated with this article can be found in the online version. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6503008","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":458178650,"identity":"911007bf-bb80-4f20-b405-c8ad632f77ac","order_by":0,"name":"Yaping Zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/klEQVRIiWNgGAWjYDACCRjJ3thw4EOFhJw88Vp4Dh88OOOMhbFhA3FaQIy05MOcbRWJDAcI6JCf3WMm8XOHRZ68Q47BYcZ5EgmMDcwPH93Ao4Vxzhkzyd4zEsWGB84YHC7cJpHHzsBmbJyDRwuzRI6ZBG+bROLGxh6DwzO3SRQzNvCwSePTwgbUIvkXpKWZx+Aw7xyJxIYDBLTwALVIg2yZz8aWcJi3gQgtEhJpxdayZyQSN/AwHzg445iEsWEzAb/Iz0jeePPtjrrE+fMfNn/4UFMnJ8/e/PAxPi1AwCLB2MDAYHAAxmfGrxys5ANIi3wDYZWjYBSMglEwQgEACkROzkcO268AAAAASUVORK5CYII=","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":true,"prefix":"","firstName":"Yaping","middleName":"","lastName":"Zhu","suffix":""},{"id":458178651,"identity":"39aba2d5-49ee-4e39-9e3b-9b43d5a7b826","order_by":1,"name":"Xinju Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Henan University of Chinese Medicine","correspondingAuthor":false,"prefix":"","firstName":"Xinju","middleName":"","lastName":"Chen","suffix":""},{"id":458178652,"identity":"8f0136a4-78dd-4a44-989d-7c95c1e545b0","order_by":2,"name":"Yanyan Lu","email":"","orcid":"","institution":"Shangqiu Medical College","correspondingAuthor":false,"prefix":"","firstName":"Yanyan","middleName":"","lastName":"Lu","suffix":""}],"badges":[],"createdAt":"2025-04-22 10:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6503008/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6503008/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83140902,"identity":"5348b87a-5583-4dd1-be39-689e4ecca00c","added_by":"auto","created_at":"2025-05-20 12:12:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":71400,"visible":true,"origin":"","legend":"\u003cp\u003eDetailed flowchart of the search process.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/802470d7aa37ee8f1b497af7.png"},{"id":83139526,"identity":"0c605144-72ad-4d60-bea6-d63a3f15a0de","added_by":"auto","created_at":"2025-05-20 12:04:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":138681,"visible":true,"origin":"","legend":"\u003cp\u003ea) Annual publication trends related to ultrasound and liver cancer from 2014 -- 2024. b) Trends in the distribution of annual research publications by country.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/bb395363d34d7d01310586c5.png"},{"id":83139527,"identity":"ed92e4d3-0ba1-4949-9165-16827f1e87cc","added_by":"auto","created_at":"2025-05-20 12:04:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183618,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork map of collaboration relationships between countries generated with R software.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/dbd3568927304b3ae16a608d.png"},{"id":83140903,"identity":"8e1604ae-a749-42b5-9682-fe1036e8ef2c","added_by":"auto","created_at":"2025-05-20 12:12:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":423819,"visible":true,"origin":"","legend":"\u003cp\u003eCooperative relationships between different countries on the connection of ultrasound and liver cancer generated with R software.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/ae5142ce1c76dcb282e927e5.png"},{"id":83139537,"identity":"c83786d8-31b5-444d-9b4b-5401c33f8c9e","added_by":"auto","created_at":"2025-05-20 12:04:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":580094,"visible":true,"origin":"","legend":"\u003cp\u003eCooperative relationships among different countries on the connection of ultrasound and liver cancer generated with VOSviewer. a) Visualization of the networks of different countries. Each node in the visualization represents a different country. The circle size is based on the number of publications. The connecting lines represent collaboration among countries. Different colors represent different items. b) Distribution of countries based on the average time of occurrence. The green and blue circles indicate more past publications, and the yellow circles indicate more recent publications.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/b62fdecf25ba7b5703ab8b1a.png"},{"id":83141197,"identity":"8c9d9e9a-3a43-4e2b-b786-3431a91b3cb5","added_by":"auto","created_at":"2025-05-20 12:20:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":255652,"visible":true,"origin":"","legend":"\u003cp\u003eCo-authorship network visualization map of institutions. Each node represents one institution. The circle size is based on the number of publications. Lines connecting nodes indicate collaboration between institutions, whereas nodes of the same color indicate membership in the same cluster.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/b1ea1e7abb26f9ddd161cf23.png"},{"id":83141198,"identity":"b568bfc2-a6c9-43db-ae68-1694e85d11e6","added_by":"auto","created_at":"2025-05-20 12:20:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":846290,"visible":true,"origin":"","legend":"\u003cp\u003ea) Co-authorship network visualization map of authors. The circles represent the number of articles published. The connecting lines represent collaboration between authors. b) Co-citation network visualization map of the authors. The size of the nodes reflects the co-occurrence frequency; the links indicate co-occurrence relationships, and nodes of the same color represent the same cluster.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/36bc2fa3f247b000e4f6a6b9.png"},{"id":83141196,"identity":"3430b72d-de45-4ae5-9a01-5b90418fa824","added_by":"auto","created_at":"2025-05-20 12:20:15","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":407834,"visible":true,"origin":"","legend":"\u003cp\u003eBibliographic coupling network visualization map of high-yielding journals. One node represents a journal, and its size is proportional to the number of publications. Different colors represent different items.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/1b89431676dc29b78850e07c.png"},{"id":83141199,"identity":"01cc4b0c-aba0-49e6-96c4-e26667c67de2","added_by":"auto","created_at":"2025-05-20 12:20:15","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":653419,"visible":true,"origin":"","legend":"\u003cp\u003ea) Co-occurrence network of keywords. The lines between nodes represent cooccurrence between different keywords. Each node represents a keyword. The circle size correlated with the frequency of occurrence. Different colors represent different items. b) Top 12 keywords with the strongest citation bursts. The red bar indicates the high number of citations in the respective year.\u003c/p\u003e","description":"","filename":"9.png","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/41d43dddb54c27e04cfadc86.png"},{"id":83143005,"identity":"c98b9ede-3df3-4cb4-a0ac-6ff07d091cb4","added_by":"auto","created_at":"2025-05-20 12:36:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4058210,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6503008/v1/f2bfcd9d-b056-4acf-9ea6-6ab40c432c3b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Application of ultrasound in liver cancer from 2014--2024: Bibliometric analysis and global trends","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eLiver cancer is a significant global health concern, and one of the leading causes of cancer-related mortality. Various risk factors contribute to the epidemiology of liver cancer, including chronic viral hepatitis, particularly hepatitis B and C infections; alcohol consumption; and nonalcoholic fatty liver disease. In many regions, the majority of patients are diagnosed at advanced stages, which severely limits treatment options and adversely affects survival rates [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Studies have shown that nearly two-thirds of liver cancer patients are diagnosed at an advanced stage, highlighting the urgent need for better early detection methods [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The clinical importance of liver cancer cannot be overstated, as its increasing incidence is projected to continue, particularly in developing countries where risk factors are prevalent [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditional diagnostic methods for liver cancer, including imaging techniques such as computed tomography (CT) and magnetic resonance imaging (MRI), have limitations such as high costs and radiation exposure [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. These methods often fail to detect small or early-stage tumors, leading to delayed diagnosis [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Additionally, relying on serum biomarkers such as alpha-fetoprotein (AFP) can be problematic. Although useful, these markers are not definitive and may produce false positives or negatives on the basis of the condition of the liver [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. As a result, there is increasing recognition of the need for more accessible and reliable diagnostic tools that can facilitate the early detection of liver cancer and improve patient outcomes.\u003c/p\u003e \u003cp\u003eUltrasound technology has evolved significantly and is now a cornerstone in the diagnosis and management of liver cancer. The basic principle of ultrasound relies on the emission of high-frequency sound waves that reflect off tissues, creating images on the basis of varying densities of the tissues encountered [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Over the years, advancements in ultrasound techniques, such as contrast-enhanced ultrasound (CEUS) and elastography, have enhanced its diagnostic capabilities [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These innovations establish ultrasound as both a diagnostic tool and a method for monitoring disease progression and treatment response. Ultrasound-guided treatment is real-time and can greatly reduce the risk of damage to various tissues, such as blood vessels and nerves [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough research on the application of ultrasound in liver cancer has increased in recent years, there is still a lack of systematic analysis of global research trends in this specific field. This study aims to highlight the importance of ultrasound technology in the detection and management of liver cancer, and to provide insights for future research directions in this area.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Data sources and retrieval strategies\u003c/h2\u003e \u003cp\u003eWe conducted a comprehensive literature search for all included studies of ultrasound and liver cancer in the Web of Science Core Collection (WoSCC, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.webofscience.com/\u003c/span\u003e\u003cspan address=\"https://www.webofscience.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) database. The search was independently performed and extracted by coauthors (YZ and YL) and those that did not comply with the following inclusion and exclusion criteria were excluded: (1) Publications from the WoSCC Science Citation Index Expanded (SCI-E) databases. (2) English language only, and (3) only original articles and reviews. (4) Exclusion of other publication types, such as early access, letters, retracted publications, corrections, proceeding papers, book chapters, editorial materials, meeting abstracts, and news items. (5) The retrieval time range is from January 01, 2014 to December 25, 2024. The detailed WoSCC database search strategy is outlined in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. Finally, 2501 eligible publications were identified, comprising 2048 articles and 453 reviews for subsequent analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSearch strategy for the Web of Science Core Collection (WoSCC).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResearch database\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeb of Science Core Collection\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCitation indexes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eScience Citation Index Expanded\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuery formulation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eultrasound (Topic) AND liver cancer (Topic)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLanguage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003etype of articles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArticles and Reviews\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSearching period\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJanuary 01, 2014 to December 25, 2024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eData collection\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexported with full records and cite reference in plain text format\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2501 publications, comprising 2048 articles and 453 reviews\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Data analyses and visualization\u003c/h2\u003e \u003cp\u003eMainstream bibliometric analysis tools including VOSviewer [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], CiteSpace, R software and Excel were employed for data analysis and visualization in this study. VOSviewer, developed by Leiden University in the Netherlands, is a powerful software for constructing and visualizing bibliometric networks on the basis of publications, countries, authors, journals and keywords. Burst keywords indicative of potential research hotspots were explored via CiteSpace [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] from Drexel University in Philadelphia, PA, USA. Additionally, the R package bibliometrix was employed to create a collaboration map of countries involved in ultrasound and liver cancer research.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1. Annual tendencies and annual citations\u003c/h2\u003e\n \u003cp\u003eIn accordance with the search strategy delineated in Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e, a total of 2728 eligible publications were identified from the WoSCC database spanning the period from January 1, 2014, to December 25, 2024. A total of 228 documents were subsequently extracted, including early access (N\u0026thinsp;=\u0026thinsp;28), letters (N\u0026thinsp;=\u0026thinsp;5), retracted publications (N\u0026thinsp;=\u0026thinsp;10), corrections (N\u0026thinsp;=\u0026thinsp;3), proceeding papers (N\u0026thinsp;=\u0026thinsp;138), book chapters (N\u0026thinsp;=\u0026thinsp;7), editorial materials (N\u0026thinsp;=\u0026thinsp;30), and meeting abstracts (N\u0026thinsp;=\u0026thinsp;7). Finally, 2501 publications were deemed eligible for further analysis, comprising 2048 research articles and 453 review papers (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Research on the use of ultrasound applications in the diagnosis and treatment of liver cancer has consistently and notably increased over the past decade (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). The number of citations for these articles has grown exponentially each year, reaching a peak of 10,140 in 2024, highlighting the increasing interest in ultrasound applications in liver cancer research (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003ea). China and the United States led in the volume of published research findings (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eb). As of December 25, 2024, the 2,501 publications were cited 52,883 times, yielding an average citation rate of 21.1 citations per publication.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2. Distribution of countries/regions\u003c/h2\u003e\n \u003cp\u003eOver the last decade, a total of 3313 research institutions from 412 countries have contributed to the literature concerning the use of ultrasound in the diagnosis and treatment of liver cancer. The articles were published in 833 journals and written by 2,501 authors. Information from the published articles was used to create a world map illustrating the cooperative relationships among the research topics in different countries (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e). Countries were ranked on the basis of the number of publications, with only the top ten nations displayed in Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e. Notably, China produced the highest volume of articles on this subject (n\u0026thinsp;=\u0026thinsp;832, 33.27%), followed by the United States (n\u0026thinsp;=\u0026thinsp;586, 23.43%), and Italy (n\u0026thinsp;=\u0026thinsp;222, 8.88%). These three countries have made significant contributions to the application of ultrasound in liver cancer research. Although the USA ranks second in the number of publications, it boasts the highest citation score, highlighting its pivotal role as a research hub in this discipline. Conversely, while China leads in publication volume, its average citation score stands at only 15.86. Figures \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e present the collaboration maps of countries related to ultrasound and liver cancer. The results show that China largely cooperated with the USA, England, Germany, France, and Italy. This shows that scientific research crosses geographic boundaries and highlights the importance of international collaboration in advancing this field.\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 10 productive countries related to ultrasound and liver cancer.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments (n)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePercentage (n/1182)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage citations\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e832\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.27%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.86\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.43%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e222\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.88%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6992\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJapan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.20%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e24.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEngland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.40%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30.79\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.80%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGermany\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.68%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouth Korea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.28%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCanada\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.56%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39.72\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.04%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3. Distribution of institutions\u003c/h2\u003e\n \u003cp\u003eVOSviewer was used to analyze and visualize the 3313 research institutions that contributed to the field. Publications (the minimum number of documents used by an organization was defined as more than five) were identified in the 249 institutions and visualized via VOSviewer (Fig. \u003cspan class=\"InternalRef\"\u003e6\u003c/span\u003e). The top 12 contributing research institutions are summarized in Table \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e. There is a close cooperative relationship among these institutions. In the past 10 years, more than half of the top 10 research institutions were from China (8/12), followed by the USA (3/12). The results indicate that institutions in China and the USA have made significant contributions to the fields of ultrasound and liver cancer. Sun Yat-sen University published the most papers (n\u0026thinsp;=\u0026thinsp;64), whereas the University of Michigan has the highest average citation value (value\u0026thinsp;=\u0026thinsp;60.28). Given the importance of collaboration, institutions need to establish closer interagency collaboration in the future.\u003c/p\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 15 institutions ranked by the number of publications.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOrganization\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage citation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSun Yat-sen University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1081\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16.89\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026zwnj;Fudan University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1090\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChongqing Medical University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChinese PLA General Hospital\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eZhejiang University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eShanghai Jiao Tong University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUniversity of Michigan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2411\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMayo Clinic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1390\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe University of Milan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eItaly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1405\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSichuan University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e23.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eStanford University\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e56.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHuazhong University of Science and Technology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4. Contributions of authors and cited authors\u003c/h2\u003e\n \u003cp\u003eThe authors (n\u0026thinsp;=\u0026thinsp;153) with a minimum productivity of 5 publications were visualized via VOSviewer software and are shown in Fig. 7a. Moreover, the collaborative relationships among these authors are displayed. The top 11 authors with the most actively published articles are also listed (Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). Among these authors, Singal, Amit G has the highest number of published articles and the highest average of citations, indicating his significant academic influence and contributions to this field. The co-citation analysis of the authors was performed via VOSviewer software (Fig.\u0026nbsp;7b). A total of 35 authors who had more than 100 citations were selected.\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 10 authors with the most actively published articles\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCountry\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAverage citation\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingal, Amit G\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e98.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiang, Ping\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1939\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYu, Jie\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e675\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e19.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eParikh, Neehar D\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUSA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1808\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e86.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCheng, Zhigang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWang, Wei\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e379\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22.29\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYu, Xiaoling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDong, Yi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e10.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHan, Zhi-Yu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiu, Fang-Yi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15.00\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWang, Zhigang\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChina\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e503\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5. Analysis of high-yielding journals\u003c/h2\u003e\n \u003cp\u003eFrom January 01, 2014 to December 25, 2024, a total of 2501 publications related to ultrasound and liver cancer were published in 833 journals, 119 of which contained at least 5 articles. A bibliographic coupling analysis of journals was performed, and a network map was generated (Fig. \u003cspan class=\"InternalRef\"\u003e8\u003c/span\u003e). The top 10 journals with the most publications in this field are listed in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e. Among them, Cancers has the highest impact factor (IF), with a value of 4.5. Additionally, the International Journal of Hyperthermia (2023 IF: 3), Medicine (2023 IF: 1.4), and World Journal of Gastroenterology (2023 IF: 4.3) are the journals with more than 40 publications each. Furthermore, combined with the information provided by VOSviewer software and the Journal Citation Reports (JCR) assessment system, we also found that the top 10 journals are concentrated in JCR categories Q1(50%) and Q2 (50%), which indicates their substantial influence in the relevant fields. As presented in Table \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e, the World Journal of Gastroenterology yielded the most citations, with 1462, followed by Ultrasound in Medicine and Biology, with 1078 citations. The International Journal of Hyperthermia has 997 citations.\u003c/p\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 10 journals with the greatest number of articles.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSource\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003edocuments\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ecitations\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2023IF\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eJCR\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUltrasound\u0026nbsp;In\u0026nbsp;Medicine\u0026nbsp;and\u0026nbsp;Biology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternational\u0026nbsp;Journal\u0026nbsp;of\u0026nbsp;Hyperthermia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e997\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMedicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e284\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorld\u0026nbsp;Journal\u0026nbsp;of\u0026nbsp;Gastroenterology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFrontiers\u0026nbsp;in\u0026nbsp;Oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eScientific\u0026nbsp;Reports\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCureus\u0026nbsp;Journal of Medical\u0026nbsp;Science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePLoS\u0026nbsp;ONE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e531\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6. Analysis of the number of citations\u003c/h2\u003e\n \u003cp\u003eThe number of citations is a critical indicator of the impact of a publication in a scientific field. These 2501 publications were counted and ranked by the number of citations. The top 10 publications are listed in Table 6. The most cited article originates from a group author of the European Association for the Study of the Liver published in the Journal of Hepatology in 2018 with 4623 citations. The second most cited article was published in Gastroenterology in 2019 with 1652 citations. Additionally, the third most cited article by Sigrist, RMS was published in Theranostics in 2017 with 1131 citations. These publications demonstrate that ultrasound plays an important role in the diagnosis and treatment of liver cancer.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e6\u003c/strong\u003e\u0026nbsp; Top 10 most cited articles related to ultrasound and liver cancer.\u003c/p\u003e\n \u003cdiv align=\"center\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"919\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eArticle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003eYears\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eJournals\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e2023 IF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003eCitations\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eEASL Clinical Practice Guidelines: Management of hepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eEuropean Assoc Study Liver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eJournal\u0026nbsp;of\u0026nbsp;Hepatology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e4623\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eBurden and Cost of Gastrointestinal,\u0026nbsp;Liver, and Pancreatic Diseases in the United States: Update 2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003ePeery, AF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eGastroenterology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1652\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eUltrasound\u0026nbsp;Elastography: Review of Techniques and Clinical Applications\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eSigrist, RMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2017\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eTheranostics\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eImage-guided Tumor Ablation: Standardization of Terminology and Reporting Criteria-A 10-Year Update\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eAhmed, M\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eRadiology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e12.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e1025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eThe global epidemiology of nonalcoholic fatty\u0026nbsp;liver\u0026nbsp;disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eYounossi, ZM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eHepatology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e827\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eSurveillance Imaging and Alpha Fetoprotein for Early Detection of Hepatocellular Carcinoma in Patients with Cirrhosis: A Meta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eTzartzeva, K\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eGastroenterology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e718\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eEpidemiology and surveillance for hepatocellular carcinoma: New trends\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eSingal, AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eJournal\u0026nbsp;of\u0026nbsp;Hepatology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eEarly Detection, Curative Treatment, and Survival Rates for Hepatocellular Carcinoma Surveillance in Patients with Cirrhosis: A Meta-analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eSingal, AG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003ePLOS\u0026nbsp;Medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e10.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e583\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003eRisk factors and prevention of hepatocellular carcinoma in the era of precision medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eFujiwara, N\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eJournal\u0026nbsp;of\u0026nbsp;Hepatology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e26.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e504\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 468px;\"\u003e\n \u003cp\u003e2019 Chinese clinical guidelines for the management of hepatocellular carcinoma: updates and insights\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 108px;\"\u003e\n \u003cp\u003eXie, DY\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 157px;\"\u003e\n \u003cp\u003eHepatobiliary\u0026nbsp;Surgery\u0026nbsp;and\u0026nbsp;Nutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 65px;\"\u003e\n \u003cp\u003e6.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 70px;\"\u003e\n \u003cp\u003e323\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.7. Analysis of keywords\u003c/h2\u003e\n \u003cp\u003eOn the basis of the literature keyword co-occurrence analysis, a detailed description of hotspot topics in the research field is presented. By analyzing the keywords of the 2501 publications, a total of 5182 keywords were identified. VOSviewer identified 36 hotspot keywords appeared at least 30 times and visualized connections through a network map (Fig. \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003ea). Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e lists the top 12 hotspot keywords with the highest frequencies. Analyzing keywords with the strongest citation bursts reveals that \u0026quot;burst words\u0026quot; are frequently cited keywords during specific periods, reflecting the academic frontier in this field. In the present study, CiteSpace software was used to identify keywords with strong citation bursts; 12 \u0026ldquo;Burst words\u0026rdquo; are presented. Figure \u003cspan class=\"InternalRef\"\u003e9\u003c/span\u003eb displays the top 12 \u0026ldquo;Burst words\u0026rdquo; related to ultrasound and liver cancer from 2014\u0026ndash;2024. The red strip indicates the period during which the burst keywords were maintained. Among them, \u0026ldquo;case report\u0026rdquo; was the most frequent keyword with strong citation bursts, which began in 2021 and ended in 2024, with the highest strength of 10.07. The second most frequent keyword, \u0026ldquo;surgery,\u0026rdquo; had a strength of 6.68 and was associated with the treatment of liver cancer under ultrasound guidance from 2014\u0026ndash;2015. Hepatocellular carcinoma(HCC)has emerged as the latest strong citation burst, indicating that it may be a hot topic in recent years.\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 12 hotspot keywords with the highest frequencies.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ekeyword\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eoccurrences\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003etotal link strength\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ehepatocellular carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eultrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eliver cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e133\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eliver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e105\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003econtrast-enhanced ultrasound\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eultrasonography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecirrhosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ecancer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003esurveillance\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003emagnetic resonance imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eradiofrequency ablation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ediagnosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eUltrasound has garnered considerable attention for its role in the early diagnosis of liver cancer. Its noninvasive nature, cost-effectiveness, and real-time imaging capabilities are highly advantageous. Early detection is pivotal for improving treatment outcomes and survival rates, as liver cancer often presents at advanced stages with a poor prognosis. Recent studies have shown that ultrasound, particularly when enhanced with contrast agents, can markedly increase the sensitivity and accuracy of liver cancer diagnosis. Furthermore, ultrasound-guided interventional therapy has also been increasingly recognized in liver cancer treatment. The real-time imaging provided by ultrasound allows doctors to locate lesions precisely during procedures such as puncture, injection, or ablation. Research has demonstrated that ultrasound-guided liver puncture and radiofrequency ablation can significantly increase treatment success rates and reduce complications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The increasing importance of ultrasound in the early diagnosis and monitoring of liver cancer cannot be overstated. Further exploration can provide valuable insights for future research directions and clinical practice in liver cancer management.\u003c/p\u003e \u003cp\u003eIn our bibliometric analysis, we identified 2501 publications, including 2048 articles and 453 reviews related to ultrasound and liver cancer research from the WoSCC database. The number of studies and citations in this field has been steadily and exponentially increasing since 2014, indicating that increasing attention has been given to this area. Notable studies have examined various aspects of this area of research, including diagnosis, CEUS, and radiofrequency ablation [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe analyzed the contributions of countries, institutions, journals, and authors. China and the USA are the top contributors to academic research, with China leading in published papers and the USA leading in citations. Considering the increasing incidence of liver cancer in China, which has imposed a considerable burden on society, research output from China, including Chinese institutions and Chinese authors, is also increasing annually [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, Chinese scholars\u0026rsquo; articles have a lower average citation count than those of the other top 10 countries do, indicating a need to prioritize impactful research over mere quantity. In contrast, France has the highest average citation count, indicating its influential role in this research area. Sun Yat-sen University in China and the University of Michigan in the USA have the highest number of published papers and average citations, respectively. The publications of Sun Yat-sen University have focus mainly on \"CEUS\" and \"ablation\", whereas those of the University of Michigan have focus mainly on \"screening\" and \"early detection\". The differences in their focuses also reflect the different national conditions they face. Their pioneering research has shed new light on the application of ultrasound in liver cancer, deepening our understanding of the diagnosis and monitoring of this deadly disease. Their groundbreaking work represents a true testament to the power of scientific research and the immense potential for collaboration in tackling some of the most pressing health challenges of our time. However, there is very little collaboration between the two research institutions. Moreover, we found that most publications are concentrated in economically developed countries and high-ranking universities, which are closely linked to research resources, culture, and international collaboration opportunities. Moreover, Singal, Amit G, a renowned scholar from the USA, holds the highest number of published articles and the highest average citation record in his field. His research revealed that sub-centimeter liver lesion ultrasound recall patterns are variable. Primary liver cancer (PLC) risk support 3\u0026ndash;6 months of ultrasound; high-risk patients may need CT/MRI. Cirrhotic patients, particularly those who are obese or have fatty liver disease, often have limited liver cancer nodule visualization via ultrasound, but the image quality can improve. Blood biomarkers show potential; however, they need validation and logistical support before they can be used clinically [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The scientific community greatly appreciates his outstanding contributions to the field.\u003c/p\u003e \u003cp\u003eUnlike other top ranked keywords such as \"liver cancer\" and \"ultrasound\", CEUS, as the fifth ranked keyword, is a truly novel, advanced ultrasound technology that has received increasing attention in the past decade. By using microbubble contrast agents, CEUS can improve the visualization of liver lesions, allowing real-time imaging that can differentiate between benign and malignant lesions on the basis of their vascular characteristics [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A meta-analysis also indicated that CEUS boasts a combined sensitivity of 92% and specificity of 93% for early liver cancer diagnosis, suggesting its potential as a primary screening tool in high-risk populations [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. CEUS offers several advantages over other imaging modalities, such as CT and MRI, including the absence of ionizing radiation and the ability to perform dynamic assessments of blood flow [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Recent advancements in CEUS technology, including the development of nanobubble agents, have further expanded its applications, allowing for improved visualization of microvascular structures and enhanced diagnostic accuracy in challenging cases [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], potentially replacing or complementing existing imaging techniques.\u003c/p\u003e \u003cp\u003eThe integration of multimodal imaging techniques represents a promising advancement in the management of liver cancer. Combining ultrasound with other imaging modalities, such as CT and MRI, can provide a more comprehensive assessment of liver lesions. Furthermore, the incorporation of artificial intelligence (AI) in multimodal imaging can enhance the interpretation of complex imaging data, leading to better differentiation between benign and malignant lesions [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. However, effectively implementing multimodal imaging involves addressing challenges in data integration, standardizing imaging protocols, and developing strong AI algorithms. Addressing these challenges will facilitate the effective use of multimodal imaging in clinical practice, ultimately improving the management of liver cancer [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study is the first to use bibliometric methods to explore the application of ultrasound in liver cancer research over the past decade. However, there are limitations. First, our publications were limited to the WOSCC database, which means that we excluded other sources such as Google Scholar, PubMed, and MEDLINE. Second, our research focused mainly on English-language literature, potentially missing high-quality literature in other languages. We hope these limitations can be addressed in future studies.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study, we explored the application of ultrasound in the diagnosis and treatment of liver cancer via bibliometric methods. The connection between ultrasound and liver cancer is a significant area of research. Through bibliometric analysis, we found that ultrasound is an important tool for the early diagnosis and treatment of liver cancer, as evidenced by a published literature review.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was supported by the Henan Provincial Special Research Project on Traditional Chinese Medicine (grant no. 2022JDZX018), the Second Batch of Henan Provincial Characteristic and Backbone Discipline Construction Projects in Traditional Chinese Medicine (grant no. STG-ZYX01-202101), and the Henan Provincial Natural Science Foundation General Program (grant no. 242300421298). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCRediT authorship contribution statement\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYanyan Lu: Writing\u0026ndash;review \u0026amp; editing, Supervision, Project administration, Methodology, Conceptualization. Xinju Chen: Writing\u0026ndash; review \u0026amp; editing, Formal analysis, Data curation, Funding acquisition. Yaping Zhu: Writing\u0026ndash;review \u0026amp; editing, Writing\u0026ndash;original draft, Visualization, Formal analysis, Data curation, Conceptualization. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of Competing Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAppendix A. Supporting information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupplementary data associated with this article can be found in the online version.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe following information was supplied regarding data availability: The raw data can be directly obtained from the Web of Science Core Collection (WoSCC) .\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e Not applicable\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate and Publish declarations\u0026nbsp;\u003c/strong\u003e Not applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cstrong\u003e\u003c/strong\u003eMcGlynn, K.A., J.L. Petrick and H.B. El-Serag, Epidemiology of Hepatocellular Carcinoma. 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Luo, Diagnostic value of liver contrast-enhanced ultrasound in early hepatocellular carcinoma: a systematic review and meta-analysis. J Gastrointest Oncol, 2023. 14(2): p. 626-635.\u003c/li\u003e\n\u003cli\u003eEmanuel, A.L., et al., Contrast-enhanced ultrasound for quantification of tissue perfusion in humans. Microcirculation, 2020. 27(1): p. e12588.\u003c/li\u003e\n\u003cli\u003eHwang, M., et al., Pediatric contrast-enhanced ultrasound: optimization of techniques and dosing. Pediatr Radiol, 2021. 51(12): p. 2147-2160.\u003c/li\u003e\n\u003cli\u003eAfyouni, S., et al., State-of-the-art imaging of hepatocellular carcinoma. J Gastrointest Surg, 2024. 28(10): p. 1717-1725.\u003c/li\u003e\n\u003cli\u003eAkbari, B., B.R. Huber and J.H. Sherman, Unlocking the Hidden Depths: Multi-Modal Integration of Imaging Mass Spectrometry-Based and Molecular Imaging Techniques. Crit Rev Anal Chem, 2025. 55(1): p. 109-138.\u003c/li\u003e\n\u003cli\u003eWeng, S., et al., Prediction of Fatty Liver Disease in a Chinese Population Using Machine-Learning Algorithms. Diagnostics (Basel), 2023. 13(6).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ultrasound, Liver cancer, Bibliometric analysis, Trends","lastPublishedDoi":"10.21203/rs.3.rs-6503008/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6503008/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Liver cancer remains one of the most prevalent and lethal malignancies worldwide, highlighting the need for effective diagnostic and monitoring strategies. Ultrasound plays a vital role in the early diagnosis, monitoring, and treatment of liver cancer. However，no bibliometric analysis has been conducted in this field before. This study aims to provide a comprehensive overview of the knowledge structure and research hotspots related to the application of ultrasound in liver cancer via bibliometric methodologies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A search was performed in the Web of Science Core Collection database for English literature studies on the application of ultrasound in liver cancer from 2014 -- 2024. Bibliometric analysis tools including VOSviewer, CiteSpace, and R Studio, were utilized to analyze global trends and research hotspots in this field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e A total of 2501 eligible publications, including 2048 articles and 453 reviews, were analyzed. In the past decade, both the annual output of publications and the citation rates have rapidly increased. The majority of published articles on this topic were originated in China (n = 832, 33.27%), followed by the United States (n = 586, 23.43%), and Italy (n = 222, 8.88%). Researchers from the United States have demonstrated high productivity, prominence, and influence in this area of research. Additionally, Sun Yat-sen University published the most papers (n = 64), whereas the University of Michigan had the highest average citation value (value = 60.28) related to research on the application of ultrasound in liver cancer. Notably, Singal, Amit G from the USA was the author with both the highest number of published articles and the highest average citation value.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eIn recent years, rapid advancements in ultrasound research for liver cancer have been reported. Increasing evidence has illustrated the crucial role of ultrasound in the early diagnosis and monitoring of liver cancer.\u003c/p\u003e","manuscriptTitle":"Application of ultrasound in liver cancer from 2014--2024: Bibliometric analysis and global trends","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-20 12:04:10","doi":"10.21203/rs.3.rs-6503008/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-02T12:03:40+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-23T17:03:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-05-20T17:58:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"54237254670207421410808747399993975697","date":"2025-05-18T08:27:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"314937043203392370698699158822305051693","date":"2025-05-16T09:51:51+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"65430189198294260564979091662319454828","date":"2025-05-16T05:35:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-16T03:25:14+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-03T00:43:58+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-05-03T00:40:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2025-04-22T10:00:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0aeeaef4-8541-4c83-a920-91524932d18b","owner":[],"postedDate":"May 20th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-08T08:08:43+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-20 12:04:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6503008","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6503008","identity":"rs-6503008","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}