Mapping the Landscape of Esophageal Cancer Research Collaboration: Insights from a Network Analysis of 49,062 Publications

Mapping the Landscape of Esophageal Cancer Research Collaboration: Insights from a Network Analysis of 49,062 Publications | 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 Mapping the Landscape of Esophageal Cancer Research Collaboration: Insights from a Network Analysis of 49,062 Publications Naruaki Ogasawara This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5063235/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aim : To map the collaborative landscape of esophageal cancer research by analyzing co-authorship networks from 2000 to 2023, based on data from the Web of Science (WoS) Core Collection, and to identify key contributors and the structural characteristics of these networks. Method: This study utilized network analysis techniques to evaluate 49,062 publications related to esophageal cancer indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed. Result: The network analysis revealed a sparse collaboration landscape with significant fragmentation across the study periods. The network density remained low throughout the years, reflecting limited collaborations relative to the potential. High clustering coefficients suggested that when collaborations occurred, they often formed tight-knit groups. The number of components increased over time, indicating growing fragmentation within the research community. Key contributors identified through degree and closeness centralities included prominent researchers with extensive networks, while those with high betweenness centrality played pivotal roles in connecting disparate research clusters. The evolving patterns demonstrated changes in collaborative behavior and the emergence of new influential figures. Conclusion: This study highlights the complex and fragmented nature of esophageal cancer research collaboration from 2000 to 2023. While clusters of active collaboration exist, the overall network shows limited integration, suggesting opportunities for enhancing global cooperation. Identifying key researchers and understanding the structure of these networks can guide future collaborative efforts, potentially accelerating advancements in esophageal cancer research. Gastroenterology & Hepatology Internal Medicine Medical Informatics Information Retrieval and Management Gastrointestinal Surgery Gastroenterology esophageal cancer co-authorship network analysis network analysis research collaboration research trend analysis research trends key researchers research strategies internal medicine planning future collaborative studies Figures Figure 1 Figure 2 Figure 3 Introduction Background and Objectives Esophageal cancer is a significant global health challenge, characterized by a high mortality rate and limited treatment options, making it a critical area of research within the medical and scientific communities 1 . The incidence of esophageal cancer varies significantly across different regions, with notable disparities between Western countries and Asia 1-3 . In Western countries, squamous cell carcinoma has been largely replaced by adenocarcinoma as the predominant type, while in Asia, particularly in China and Japan, squamous cell carcinoma remains the most common form 2-3 . This variation reflects differences in risk factors, such as smoking, alcohol consumption, diet, and the prevalence of gastroesophageal reflux disease (GERD). Globally, GERD affects millions of people, with varying prevalence rates across different regions. It is prevalent in Western countries due to lifestyle factors like diet, obesity, and sedentary behavior. At the same time, its prevalence in Asia has been historically lower but is rising due to changes in dietary habits and increasing obesity rates 4 . These regional differences underscore the importance of collaborative research efforts that address the unique challenges and epidemiological trends associated with esophageal cancer worldwide. Despite progress in understanding GERD’s mechanisms and treatment options, collaboration among researchers remains crucial for further advancements. Understanding the evolution of research collaboration in esophageal cancer is essential to advancing the field and improving patient outcomes. Co-authorship network analysis offers a valuable framework for exploring the structure and dynamics of research collaboration, allowing us to identify key contributors, influential research groups, and the overall patterns of cooperation that drive scientific progress. This study aims to map the landscape of esophageal cancer research collaboration by analyzing the co-authorship network based on 49,062 publications from the Web of Science Core Collection, covering the period from 2000 to 2023. By examining both macro-level and micro-level network indicators, this research seeks to provide insights into the collaborative structures that underpin advancements in esophageal cancer research. Scope of the Study This study examines publications related to esophageal cancer research indexed in the WoS Core Collection database between 2000 and 2023. A total of 49,062 articles were selected for analysis, providing a comprehensive overview of the collaborative landscape within this specialized field over the past two decades. The dataset ensures the inclusion of the most recent publications (as of September 2024). The analysis will focus on constructing and evaluating co-authorship networks using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (the degree to which nodes tend to cluster together), number of components (distinct connected subgroups within the network), and average path length (the average distance between nodes). At the micro-level, I will assess degree centrality (the number of direct connections each node has), closeness centrality (how close a node is to all other nodes), and betweenness centrality (the extent to which a node lies on the shortest path between other nodes). These metrics will help illuminate the structure and dynamics of researcher collaborations in this field. Significance of the Study This study holds significant value in the context of esophageal cancer research by offering a detailed exploration of the collaborative landscape within this field. Identifying major researchers and institutions involved in esophageal cancer research can help highlight leading contributors and emerging leaders. Furthermore, evaluating the progression of international collaborative research and its impact is essential for understanding how global partnerships contribute to advancements in this area. The analysis of network structures and their evolution over time can reveal critical trends, such as shifts in research focus or the emergence of new collaborative clusters. By providing a clear picture of the current state of research and collaboration in esophageal cancer, this study not only enhances our understanding of existing networks but also sheds light on future directions and potential areas for new partnerships. The findings underscore the importance of international collaboration in addressing the complex challenges associated with esophageal cancer, highlighting the role of network analysis as a powerful tool for guiding future research strategies and fostering global cooperation. Material and methods The present study investigates the co-authorship patterns in esophageal cancer research papers. I utilized the WoS Core Collection database, conducting a "Topic Search" with the keyword “esophageal cancer" to analyze a total of 49,062 articles published between 2000 and 2023 (as of September 2024). In this analysis, I examined who collaborated with whom in co-authoring these papers. I conducted network analysis using the Python programming language (version 3.10.5) within the integrated development environment (IDE) PyCharm (software version 2022.1.3). This study employed methodology-established principles of social network analysis 5 . I carried out the analysis in two main parts: Macro-level Metrics: Network Density: Calculated as the ratio of the number of edges to the maximum possible edges Between all nodes. Clustering Coefficient: Measured the extent to which nodes form clusters by considering the number of edges among neighboring nodes and calculating the average. Components: Identified and counted the number of subgraphs (components) where nodes are mutually connected. Average Path Length: Evaluated the average "distance" between nodes by calculating the overall average path length in the network 6 . Micro-level Metrics: Degree Centrality: Measured the importance of each node by counting the number of edges it has in the network. Closeness Centrality: Defined as the inverse of the sum of the shortest path lengths from a node to all other nodes, measuring how close each node is to others in the network. Betweenness Centrality: Assessed the extent to which a node lies on the shortest paths between other nodes, indicating its importance in information transmission within the network 6-7 . The significance of these macro-level metrics in understanding the structure of scientific collaboration networks and these micro-level centrality measures in scientific collaboration networks has been well documented and used 6-7 . Through these analyses, I can identify collaborative relationships and influential researchers in esophageal cancer research. This information may be useful for understanding research trends and planning future collaborative studies. Results The study analyzed the co-authorship network of researchers in esophageal cancer research, focusing on the periods from 2000 to 2023. The analysis was conducted using data from the WoS Core Collection and utilized both macro and micro-level network metrics to understand the evolution of collaborative networks in this field. 2000–2009 Network Analysis During the 2000–2009 period, the co-authorship network showed significant collaborative efforts observed among researchers (Fig. 1 ). The network's density was calculated to be 0.000274 (Table 1), indicating a sparse network where only a small fraction of potential collaborations was realized (Fig. 1 ). The average clustering coefficient was high at 0.884 (Table 1), suggesting that when collaborations existed, they often formed tight clusters, reflecting cohesive subgroups of researchers working closely together (Fig. 1 ). The network contained 2,179 components (Table 1), highlighting the fragmented nature of research during this period, with numerous isolated groups of co-authors (Fig. 1 ). The average distance within the network was infinite, indicating the presence of disconnected components 8 . At the micro level, degree centrality was used to identify key researchers most actively collaborating within the network. The top researchers by degree centrality included Taylor, PR (0.0057), Kato, H (0.0054), and Shimada, Y (0.0053), among others, suggesting their prominent roles in fostering collaboration (Table 2). Closeness centrality analysis revealed that researchers like Repici, A (0.0473) and Conio, M (0.0472) were central figures in the network, maintaining close proximity to many other researchers (Table 3). Betweenness centrality identified Airoldi, M (0.1898) and Orecchia, R (0.1896) as pivotal connectors within the network, facilitating interactions between otherwise unconnected researchers (Table 4). 2010–2019 Network Analysis The 2010–2019 period showed a slight reduction in network density to 0.000179 (Table 1), indicating continued sparsity in collaboration relative to the size of the network (Fig. 2 ). The average clustering coefficient decreased slightly to 0.871 (Table 1), which still points to strong clustering behavior within active collaborative groups (Fig. 2 ). The number of components increased to 2,974 (Table 1), reflecting a further fragmentation of the research community into more distinct collaborative clusters. As in the previous period, the average distance remained infinite, reinforcing the disconnected nature of the network 8 . Degree centrality analysis for this period highlighted Malekzadeh, Reza (0.0085), Abnet, Christian C. (0.0080), and Dawsey, Sanford M. (0.0080) as leading collaborators, suggesting that they were central figures in esophageal cancer research collaborations (Table 2). Closeness centrality identified Hofstetter, Wayne L. (0.2251) and Dawsey, Sanford M. (0.2231) as being particularly influential due to their strategic positions that minimized the distance to other researchers (Table 3). Betweenness centrality analysis showed that Wijnhoven, B. P. L. (0.0345) and Li, Jian (0.0315) were important intermediaries, playing key roles in linking different clusters of researchers (Table 4). 2020–2023 Network Analysis During the 2000–2023 period, the co-authorship network of esophageal cancer research exhibited a complex and highly fragmented structure (Fig. 3 ). The network density was calculated at 0.000318 (Table 1), indicating a sparse structure with relatively limited collaborations compared to the potential connections that could exist within the network. The average clustering coefficient was 0.886 (Table 1), suggesting that while researchers often collaborated within tight clusters (Fig. 3 ), the broader network was not well-integrated across different groups. The network consisted of 2,218 components (Table 1), reflecting a significant number of isolated research clusters, which points to a continued lack of unified collaborative efforts across the field. The average distance between nodes was infinite, reinforcing the notion of persistent disconnectedness among various components of the network. 8 . At the micro level, analysis of degree centrality identified van Hillegersberg, R. (0.0108), Ruurda, J. P. (0.0106), and Singh, P. (0.0103) as leading figures in terms of collaborative activity, highlighting their prominent roles and extensive networks within the field. These researchers maintained high degrees of connectivity, indicating their involvement in numerous collaborations across various subgroups (Table 2). Closeness centrality metrics revealed Li, Yin (0.2351), Lordick, Florian (0.2337), and Piessen, Guillaume (0.2323) as key figures who were strategically positioned within the network to efficiently reach other researchers, thus facilitating the flow of information and fostering collaborations (Table 3). Betweenness centrality analysis identified Lordick, Florian (0.0298), Wang, Xin (0.0224), and Piessen, Guillaume (0.0206) as pivotal intermediaries who played crucial roles in bridging otherwise disconnected clusters, enabling cross-collaborative efforts that might not have occurred without their involvement (Table 4). Discussion The analysis of the co-authorship network in esophageal cancer research from 2000 to 2023 reveals significant insights into the collaborative landscape of this field. By examining both macro and micro-level network metrics, we can better understand the evolution and current state of research collaborations. The network density, a measure of the proportion of actual collaborations to potential collaborations, remained low throughout the study period, indicating a sparse network. Despite the increase in the number of publications and researchers over the years, the density did not show a substantial rise. This suggests that while more researchers are contributing to the field, they are not necessarily collaborating more frequently. The clustering coefficient, which measures the degree to which researchers form tight-knit groups, remained high, indicating that when collaborations did occur, they often formed cohesive subgroups. This pattern was consistent across all three periods analyzed (2000–2009, 2010–2019, and 2020–2023). The number of components, or isolated groups of researchers, increased over time, reflecting a growing fragmentation in the research community. This fragmentation could be due to the emergence of specialized subfields within esophageal cancer research, leading to more isolated clusters of researchers. The average distance within the network remained infinite, highlighting the persistent disconnectedness among various components of the network. At the micro level, degree centrality identified key researchers who played prominent roles in fostering collaborations. Researchers such as Taylor, PR, Kato, H, and Shimada, Y in the early period, and van Hillegersberg, R, Ruurda, J. P., and Singh, P. in the later period, were central figures in the network. These researchers had high degrees of connectivity, indicating their involvement in numerous collaborations across various subgroups. Closeness centrality metrics revealed researchers who were strategically positioned within the network to efficiently reach other researchers. Figures like Repici, A, Conio, M, and later Li, Yin, and Lordick, Florian, were crucial in facilitating the flow of information and fostering collaborations. Betweenness centrality analysis identified pivotal intermediaries such as Airoldi, M, Orecchia, R, and later Lordick, Florian, and Wang, Xin, who played key roles in bridging otherwise disconnected clusters, enabling cross-collaborative efforts. The findings of this study have several implications for the future of esophageal cancer research. The persistent fragmentation and low network density suggest a need for initiatives to promote broader collaboration across different research groups. Encouraging interdisciplinary research and creating platforms for researchers to connect and share their work could help bridge the gaps identified in this study. Moreover, the identification of key researchers and intermediaries provides valuable insights for policymakers and funding agencies. Supporting these central figures and fostering their collaborative efforts could enhance the overall connectivity and productivity of the research network. In conclusion, this study provides a comprehensive overview of the collaborative landscape in esophageal cancer research. By understanding the structure and dynamics of the co-authorship network, we can identify areas for improvement and take steps to foster a more integrated and collaborative research community. Conclusion The co-authorship network analysis of esophageal cancer research from 2000 to 2023 provides valuable insights into the collaborative landscape within this field. The network's overall density was consistently low across the studied periods, indicating a sparse level of collaboration relative to the potential connections that could exist. This suggests that while the number of researchers and publications has grown, collaborative interactions have not scaled proportionately, pointing to a fragmented research community. The high clustering coefficient throughout the periods indicates that existing collaborations often occur within tight clusters, highlighting cohesive subgroups of researchers who work closely together. However, the network also revealed a significant number of components, suggesting the presence of many isolated groups and a lack of interconnectedness across the broader network. Micro-level indicators identified key contributors within the network. Degree centrality highlighted prominent researchers such as Richard van Hillegersberg (University Medical Center Utrecht, Netherlands), Jelle P. Ruurda (University Medical Center Utrecht, Netherlands), and P. Singh (Bristol-Myers Squibb, Princeton, United States), who demonstrated high levels of collaborative activity. Closeness centrality metrics identified researchers like Yin Li (ZhengzZhou University, China) and Florian Lordick (University of Leipzig, Germany) as strategically positioned within the network, facilitating efficient communication and collaboration. Betweenness centrality underscored the importance of researchers such as Florian Lordick (University of Leipzig, Germany) and Xin Wang (National Cancer Center, National Clinical Research Center for Cancer, Beijing, China) as pivotal intermediaries who bridge gaps between otherwise disconnected clusters, enabling broader collaborative efforts that are essential for advancing the field. The findings underscore the need for increased interconnectedness among researchers to foster a more unified and cohesive research community. The persistence of numerous isolated clusters suggests missed opportunities for collaboration, which could hinder the sharing of knowledge and slow the progress of esophageal cancer research. Enhancing collaborative networks by encouraging cross-group interactions and international partnerships may be key to overcoming these challenges. Overall, this study highlights the critical role of network analysis in understanding the structure of research collaborations in esophageal cancer. By identifying key researchers and mapping the landscape of collaboration, the study provides a foundation for future efforts to strengthen the research community. This enhanced understanding of the collaborative dynamics can guide strategic decisions aimed at fostering a more integrated and productive research environment, ultimately contributing to advancements in the field and improved patient outcomes. Abbreviations WoS, Web of Science; IDE, Integrated Development Environment, GERD, Gastroesophageal Reflux Disease. Declarations Funding statement: none Conflict of interest disclosure statement: none Ethics approval statement: not applicable for this article. References Klingelhöfer D, Zhu Y, Braun M, Brüggmann D, Schöffel N, Groneberg DA. A world map of esophagus cancer research: a critical accounting. J Transl Med. 2019 May 10;17(1):150. Grille VJ, Campbell S, Gibbs JF, Bauer TL. Esophageal cancer: the rise of adenocarcinoma over squamous cell carcinoma in the Asian belt. J Gastrointest Oncol. 2021 Jul;12(Suppl 2):S339-S349. Zhang HZ, Jin GF, Shen HB. Epidemiologic differences in esophageal cancer between Asian and Western populations. Chin J Cancer. 2012 Jun;31(6):281-6. Ogasawara, N. Co-Authorship Network Analysis in Gastroesophageal Reflux Disease Research: Evaluating Collaboration and Structural Changes from 2000 to 2023. Preprints 2024, 2024090408. https://doi.org/10.20944/preprints202409.0408.v1. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press. https://doi.org/10.1017/CBO9780511815478. Newman, M. (2001), 'Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality', Phys. Rev. E 64 (1), 016132. Newman ME. The structure of scientific collaboration networks. Proc Natl Acad Sci USA. 2001 Jan 16;98(2):404-9 Barabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999 Oct 15;286(5439):509-12. Table Tables 1 to 4 are available in the Supplementary Files section Additional Declarations The authors declare no competing interests. Supplementary Files Table1.xlsx Network Metrics Table2.xlsx Top 20 Nodes by Degree Centrality Table3.xlsx Top 20 Nodes by Closeness Centrality Table4.xlsx Top 20 Nodes by Betweenness Centrality Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eMapping the Landscape of Esophageal Cancer Research Collaboration: Insights from a Network Analysis of 49,062 Publications\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003e\u003cstrong\u003eBackground and Objectives\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEsophageal cancer is a significant global health challenge, characterized by a high mortality rate and limited treatment options, making it a critical area of research within the medical and scientific communities \u003csup\u003e1\u003c/sup\u003e. The incidence of esophageal cancer varies significantly across different regions, with notable disparities between Western countries and Asia \u003csup\u003e1-3\u003c/sup\u003e. In Western countries, squamous cell carcinoma has been largely replaced by adenocarcinoma as the predominant type, while in Asia, particularly in China and Japan, squamous cell carcinoma remains the most common form \u003csup\u003e2-3\u003c/sup\u003e. This variation reflects differences in risk factors, such as smoking, alcohol consumption, diet, and the prevalence of gastroesophageal reflux disease (GERD). Globally, GERD affects millions of people, with varying prevalence rates across different regions. It is prevalent in Western countries due to lifestyle factors like diet, obesity, and sedentary behavior. At the same time, its prevalence in Asia has been historically lower but is rising due to changes in dietary habits and increasing obesity rates \u003csup\u003e4\u003c/sup\u003e. These regional differences underscore the importance of collaborative research efforts that address the unique challenges and epidemiological trends associated with esophageal cancer worldwide. Despite progress in understanding GERD’s mechanisms and treatment options, collaboration among researchers remains crucial for further advancements.\u003c/p\u003e\n\u003cp\u003eUnderstanding the evolution of research collaboration in esophageal cancer is essential to advancing the field and improving patient outcomes. Co-authorship network analysis offers a valuable framework for exploring the structure and dynamics of research collaboration, allowing us to identify key contributors, influential research groups, and the overall patterns of cooperation that drive scientific progress. This study aims to map the landscape of esophageal cancer research collaboration by analyzing the co-authorship network based on 49,062 publications from the Web of Science Core Collection, covering the period from 2000 to 2023. By examining both macro-level and micro-level network indicators, this research seeks to provide insights into the collaborative structures that underpin advancements in esophageal cancer research.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eScope of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study examines publications related to esophageal cancer research indexed in the WoS Core Collection database between 2000 and 2023. A total of 49,062 articles were selected for analysis, providing a comprehensive overview of the collaborative landscape within this specialized field over the past two decades. The dataset ensures the inclusion of the most recent publications (as of September 2024). The analysis will focus on constructing and evaluating co-authorship networks using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (the degree to which nodes tend to cluster together), number of components (distinct connected subgroups within the network), and average path length (the average distance between nodes). At the micro-level, I will assess degree centrality (the number of direct connections each node has), closeness centrality (how close a node is to all other nodes), and betweenness centrality (the extent to which a node lies on the shortest path between other nodes). These metrics will help illuminate the structure and dynamics of researcher collaborations in this field.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSignificance of the Study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study holds significant value in the context of esophageal cancer research by offering a detailed exploration of the collaborative landscape within this field. Identifying major researchers and institutions involved in esophageal cancer research can help highlight leading contributors and emerging leaders. Furthermore, evaluating the progression of international collaborative research and its impact is essential for understanding how global partnerships contribute to advancements in this area. The analysis of network structures and their evolution over time can reveal critical trends, such as shifts in research focus or the emergence of new collaborative clusters.\u003c/p\u003e\n\u003cp\u003eBy providing a clear picture of the current state of research and collaboration in esophageal cancer, this study not only enhances our understanding of existing networks but also sheds light on future directions and potential areas for new partnerships. The findings underscore the importance of international collaboration in addressing the complex challenges associated with esophageal cancer, highlighting the role of network analysis as a powerful tool for guiding future research strategies and fostering global cooperation.\u003c/p\u003e"},{"header":"Material and methods","content":"\u003cp\u003eThe present study investigates the co-authorship patterns in esophageal cancer research papers. I utilized the WoS Core Collection database, conducting a \"Topic Search\" with the keyword “esophageal\u0026nbsp;cancer\" to analyze a total of 49,062 articles published between 2000 and 2023 (as of September 2024). In this analysis, I examined who collaborated with whom in co-authoring these papers. I conducted network analysis using the Python programming language (version 3.10.5) within the integrated development environment (IDE) PyCharm (software version 2022.1.3). This study employed methodology-established principles of social network analysis \u003csup\u003e5\u003c/sup\u003e. I carried out the analysis in two main parts:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMacro-level Metrics:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNetwork Density:\u0026nbsp;\u003c/em\u003eCalculated as the ratio of the number of edges to the maximum possible edges Between all nodes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eClustering Coefficient:\u0026nbsp;\u003c/em\u003eMeasured the extent to which nodes form clusters by considering the number of edges among neighboring nodes and calculating the average.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eComponents:\u0026nbsp;\u003c/em\u003eIdentified and counted the number of subgraphs (components) where nodes are mutually connected.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAverage Path Length:\u0026nbsp;\u003c/em\u003eEvaluated the average \"distance\" between nodes by calculating the overall average path length in the network \u003csup\u003e6\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eMicro-level Metrics:\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eDegree Centrality:\u0026nbsp;\u003c/em\u003eMeasured the importance of each node by counting the number of edges it has in the network.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCloseness Centrality:\u0026nbsp;\u003c/em\u003eDefined as the inverse of the sum of the shortest path lengths from a node to all other nodes, measuring how close each node is to others in the network.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBetweenness Centrality:\u0026nbsp;\u003c/em\u003eAssessed the extent to which a node lies on the shortest paths between other nodes, indicating its importance in information transmission within the network \u003csup\u003e6-7\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe significance of these macro-level metrics in understanding the structure of scientific collaboration networks and these micro-level centrality measures in scientific collaboration networks has been well documented and used\u0026nbsp;\u003csup\u003e6-7\u003c/sup\u003e. Through these analyses, I can identify collaborative relationships and influential researchers in esophageal cancer research. This information may be useful for understanding research trends and planning future collaborative studies.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eThe study analyzed the co-authorship network of researchers in esophageal cancer research, focusing on the periods from 2000 to 2023. The analysis was conducted using data from the WoS Core Collection and utilized both macro and micro-level network metrics to understand the evolution of collaborative networks in this field.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2000\u0026ndash;2009 Network Analysis\u003c/h2\u003e \u003cp\u003eDuring the 2000\u0026ndash;2009 period, the co-authorship network showed significant collaborative efforts observed among researchers (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The network's density was calculated to be 0.000274 (Table\u0026nbsp;1), indicating a sparse network where only a small fraction of potential collaborations was realized (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average clustering coefficient was high at 0.884 (Table\u0026nbsp;1), suggesting that when collaborations existed, they often formed tight clusters, reflecting cohesive subgroups of researchers working closely together (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The network contained 2,179 components (Table\u0026nbsp;1), highlighting the fragmented nature of research during this period, with numerous isolated groups of co-authors (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The average distance within the network was infinite, indicating the presence of disconnected components \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the micro level, degree centrality was used to identify key researchers most actively collaborating within the network. The top researchers by degree centrality included Taylor, PR (0.0057), Kato, H (0.0054), and Shimada, Y (0.0053), among others, suggesting their prominent roles in fostering collaboration (Table\u0026nbsp;2). Closeness centrality analysis revealed that researchers like Repici, A (0.0473) and Conio, M (0.0472) were central figures in the network, maintaining close proximity to many other researchers (Table\u0026nbsp;3). Betweenness centrality identified Airoldi, M (0.1898) and Orecchia, R (0.1896) as pivotal connectors within the network, facilitating interactions between otherwise unconnected researchers (Table\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2010–2019 Network Analysis\u003c/h3\u003e\n\u003cp\u003eThe 2010\u0026ndash;2019 period showed a slight reduction in network density to 0.000179 (Table\u0026nbsp;1), indicating continued sparsity in collaboration relative to the size of the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The average clustering coefficient decreased slightly to 0.871 (Table\u0026nbsp;1), which still points to strong clustering behavior within active collaborative groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The number of components increased to 2,974 (Table\u0026nbsp;1), reflecting a further fragmentation of the research community into more distinct collaborative clusters. As in the previous period, the average distance remained infinite, reinforcing the disconnected nature of the network \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDegree centrality analysis for this period highlighted Malekzadeh, Reza (0.0085), Abnet, Christian C. (0.0080), and Dawsey, Sanford M. (0.0080) as leading collaborators, suggesting that they were central figures in esophageal cancer research collaborations (Table\u0026nbsp;2). Closeness centrality identified Hofstetter, Wayne L. (0.2251) and Dawsey, Sanford M. (0.2231) as being particularly influential due to their strategic positions that minimized the distance to other researchers (Table\u0026nbsp;3). Betweenness centrality analysis showed that Wijnhoven, B. P. L. (0.0345) and Li, Jian (0.0315) were important intermediaries, playing key roles in linking different clusters of researchers (Table\u0026nbsp;4).\u003c/p\u003e\n\u003ch3\u003e2020–2023 Network Analysis\u003c/h3\u003e\n\u003cp\u003eDuring the 2000\u0026ndash;2023 period, the co-authorship network of esophageal cancer research exhibited a complex and highly fragmented structure (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The network density was calculated at 0.000318 (Table\u0026nbsp;1), indicating a sparse structure with relatively limited collaborations compared to the potential connections that could exist within the network. The average clustering coefficient was 0.886 (Table\u0026nbsp;1), suggesting that while researchers often collaborated within tight clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), the broader network was not well-integrated across different groups. The network consisted of 2,218 components (Table\u0026nbsp;1), reflecting a significant number of isolated research clusters, which points to a continued lack of unified collaborative efforts across the field. The average distance between nodes was infinite, reinforcing the notion of persistent disconnectedness among various components of the network. \u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAt the micro level, analysis of degree centrality identified van Hillegersberg, R. (0.0108), Ruurda, J. P. (0.0106), and Singh, P. (0.0103) as leading figures in terms of collaborative activity, highlighting their prominent roles and extensive networks within the field. These researchers maintained high degrees of connectivity, indicating their involvement in numerous collaborations across various subgroups (Table\u0026nbsp;2). Closeness centrality metrics revealed Li, Yin (0.2351), Lordick, Florian (0.2337), and Piessen, Guillaume (0.2323) as key figures who were strategically positioned within the network to efficiently reach other researchers, thus facilitating the flow of information and fostering collaborations (Table\u0026nbsp;3). Betweenness centrality analysis identified Lordick, Florian (0.0298), Wang, Xin (0.0224), and Piessen, Guillaume (0.0206) as pivotal intermediaries who played crucial roles in bridging otherwise disconnected clusters, enabling cross-collaborative efforts that might not have occurred without their involvement (Table\u0026nbsp;4).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe analysis of the co-authorship network in esophageal cancer research from 2000 to 2023 reveals significant insights into the collaborative landscape of this field. By examining both macro and micro-level network metrics, we can better understand the evolution and current state of research collaborations.\u003c/p\u003e \u003cp\u003eThe network density, a measure of the proportion of actual collaborations to potential collaborations, remained low throughout the study period, indicating a sparse network. Despite the increase in the number of publications and researchers over the years, the density did not show a substantial rise. This suggests that while more researchers are contributing to the field, they are not necessarily collaborating more frequently. The clustering coefficient, which measures the degree to which researchers form tight-knit groups, remained high, indicating that when collaborations did occur, they often formed cohesive subgroups. This pattern was consistent across all three periods analyzed (2000\u0026ndash;2009, 2010\u0026ndash;2019, and 2020\u0026ndash;2023).\u003c/p\u003e \u003cp\u003eThe number of components, or isolated groups of researchers, increased over time, reflecting a growing fragmentation in the research community. This fragmentation could be due to the emergence of specialized subfields within esophageal cancer research, leading to more isolated clusters of researchers. The average distance within the network remained infinite, highlighting the persistent disconnectedness among various components of the network.\u003c/p\u003e \u003cp\u003eAt the micro level, degree centrality identified key researchers who played prominent roles in fostering collaborations. Researchers such as Taylor, PR, Kato, H, and Shimada, Y in the early period, and van Hillegersberg, R, Ruurda, J. P., and Singh, P. in the later period, were central figures in the network. These researchers had high degrees of connectivity, indicating their involvement in numerous collaborations across various subgroups.\u003c/p\u003e \u003cp\u003eCloseness centrality metrics revealed researchers who were strategically positioned within the network to efficiently reach other researchers. Figures like Repici, A, Conio, M, and later Li, Yin, and Lordick, Florian, were crucial in facilitating the flow of information and fostering collaborations. Betweenness centrality analysis identified pivotal intermediaries such as Airoldi, M, Orecchia, R, and later Lordick, Florian, and Wang, Xin, who played key roles in bridging otherwise disconnected clusters, enabling cross-collaborative efforts.\u003c/p\u003e \u003cp\u003eThe findings of this study have several implications for the future of esophageal cancer research. The persistent fragmentation and low network density suggest a need for initiatives to promote broader collaboration across different research groups. Encouraging interdisciplinary research and creating platforms for researchers to connect and share their work could help bridge the gaps identified in this study.\u003c/p\u003e \u003cp\u003eMoreover, the identification of key researchers and intermediaries provides valuable insights for policymakers and funding agencies. Supporting these central figures and fostering their collaborative efforts could enhance the overall connectivity and productivity of the research network.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides a comprehensive overview of the collaborative landscape in esophageal cancer research. By understanding the structure and dynamics of the co-authorship network, we can identify areas for improvement and take steps to foster a more integrated and collaborative research community.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe co-authorship network analysis of esophageal cancer research from 2000 to 2023 provides valuable insights into the collaborative landscape within this field. The network's overall density was consistently low across the studied periods, indicating a sparse level of collaboration relative to the potential connections that could exist. This suggests that while the number of researchers and publications has grown, collaborative interactions have not scaled proportionately, pointing to a fragmented research community. The high clustering coefficient throughout the periods indicates that existing collaborations often occur within tight clusters, highlighting cohesive subgroups of researchers who work closely together. However, the network also revealed a significant number of components, suggesting the presence of many isolated groups and a lack of interconnectedness across the broader network.\u003c/p\u003e \u003cp\u003eMicro-level indicators identified key contributors within the network. Degree centrality highlighted prominent researchers such as Richard van Hillegersberg (University Medical Center Utrecht, Netherlands), Jelle P. Ruurda (University Medical Center Utrecht, Netherlands), and P. Singh (Bristol-Myers Squibb, Princeton, United States), who demonstrated high levels of collaborative activity. Closeness centrality metrics identified researchers like Yin Li (ZhengzZhou University, China) and Florian Lordick (University of Leipzig, Germany) as strategically positioned within the network, facilitating efficient communication and collaboration. Betweenness centrality underscored the importance of researchers such as Florian Lordick (University of Leipzig, Germany) and Xin Wang (National Cancer Center, National Clinical Research Center for Cancer, Beijing, China) as pivotal intermediaries who bridge gaps between otherwise disconnected clusters, enabling broader collaborative efforts that are essential for advancing the field.\u003c/p\u003e \u003cp\u003eThe findings underscore the need for increased interconnectedness among researchers to foster a more unified and cohesive research community. The persistence of numerous isolated clusters suggests missed opportunities for collaboration, which could hinder the sharing of knowledge and slow the progress of esophageal cancer research. Enhancing collaborative networks by encouraging cross-group interactions and international partnerships may be key to overcoming these challenges.\u003c/p\u003e \u003cp\u003eOverall, this study highlights the critical role of network analysis in understanding the structure of research collaborations in esophageal cancer. By identifying key researchers and mapping the landscape of collaboration, the study provides a foundation for future efforts to strengthen the research community. This enhanced understanding of the collaborative dynamics can guide strategic decisions aimed at fostering a more integrated and productive research environment, ultimately contributing to advancements in the field and improved patient outcomes.\u003c/p\u003e"},{"header":"Abbreviations","content":"WoS, Web of Science; IDE, Integrated Development Environment, GERD, Gastroesophageal Reflux Disease."},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding statement:\u003c/strong\u003e none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest disclosure statement:\u003c/strong\u003e none\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement:\u003c/strong\u003e not applicable for this article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eKlingelh\u0026ouml;fer D, Zhu Y, Braun M, Br\u0026uuml;ggmann D, Sch\u0026ouml;ffel N, Groneberg DA. A world map of esophagus cancer research: a critical accounting. J Transl Med. 2019 May 10;17(1):150.\u003c/li\u003e\n \u003cli\u003eGrille VJ, Campbell S, Gibbs JF, Bauer TL. Esophageal cancer: the rise of adenocarcinoma over squamous cell carcinoma in the Asian belt. J Gastrointest Oncol. 2021 Jul;12(Suppl 2):S339-S349.\u003c/li\u003e\n \u003cli\u003eZhang HZ, Jin GF, Shen HB. Epidemiologic differences in esophageal cancer between Asian and Western populations. Chin J Cancer. 2012 Jun;31(6):281-6.\u003c/li\u003e\n \u003cli\u003eOgasawara, N. Co-Authorship Network Analysis in Gastroesophageal Reflux Disease Research: Evaluating Collaboration and Structural Changes from 2000 to 2023. Preprints 2024, 2024090408. https://doi.org/10.20944/preprints202409.0408.v1.\u003c/li\u003e\n \u003cli\u003eWasserman, S., \u0026amp; Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press. https://doi.org/10.1017/CBO9780511815478.\u003c/li\u003e\n \u003cli\u003eNewman, M. (2001), \u0026apos;Scientific collaboration networks. II. Shortest paths, weighted networks, and centrality\u0026apos;, Phys. Rev. E 64 (1), 016132.\u003c/li\u003e\n \u003cli\u003eNewman ME. The structure of scientific collaboration networks. Proc Natl Acad Sci USA. 2001 Jan 16;98(2):404-9\u003c/li\u003e\n \u003cli\u003eBarabasi AL, Albert R. Emergence of scaling in random networks. Science. 1999 Oct 15;286(5439):509-12.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTables 1 to 4 are available in the Supplementary Files section\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"The Japanese Society of Internal Medicine","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Gastroenterology, esophageal cancer, co-authorship network analysis, network analysis, research collaboration, research trend analysis, research trends, key researchers, research strategies, internal medicine, planning future collaborative studies","lastPublishedDoi":"10.21203/rs.3.rs-5063235/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5063235/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eAim\u003c/strong\u003e: To map the collaborative landscape of esophageal cancer research by analyzing co-authorship networks from 2000 to 2023, based on data from the Web of Science (WoS) Core Collection, and to identify key contributors and the structural characteristics of these networks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThis study utilized network analysis techniques to evaluate 49,062 publications related to esophageal cancer indexed in the WoS Core Collection between 2000 and 2023. The analysis was conducted using Python (Version 3.10.5) in the PyCharm development environment (Software Version 2022.1.3). The co-authorship networks were assessed using macro-level indicators such as network density (the ratio of actual to possible connections), clustering coefficient (degree of node clustering), number of components (distinct connected subgroups), and average path length (average distance between nodes). Micro-level indicators including degree centrality (importance based on the number of connections), closeness centrality (proximity to other nodes), and betweenness centrality (frequency of a node on the shortest paths between others) were also analyzed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResult:\u003c/strong\u003e The network analysis revealed a sparse collaboration landscape with significant fragmentation across the study periods. The network density remained low throughout the years, reflecting limited collaborations relative to the potential. High clustering coefficients suggested that when collaborations occurred, they often formed tight-knit groups. The number of components increased over time, indicating growing fragmentation within the research community. Key contributors identified through degree and closeness centralities included prominent researchers with extensive networks, while those with high betweenness centrality played pivotal roles in connecting disparate research clusters. The evolving patterns demonstrated changes in collaborative behavior and the emergence of new influential figures.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis study highlights the complex and fragmented nature of esophageal cancer research collaboration from 2000 to 2023. While clusters of active collaboration exist, the overall network shows limited integration, suggesting opportunities for enhancing global cooperation. Identifying key researchers and understanding the structure of these networks can guide future collaborative efforts, potentially accelerating advancements in esophageal cancer research.\u003c/p\u003e","manuscriptTitle":"Mapping the Landscape of Esophageal Cancer Research Collaboration: Insights from a Network Analysis of 49,062 Publications","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-20 06:26:34","doi":"10.21203/rs.3.rs-5063235/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"3ed88d0e-9989-48b0-8de0-6032abb23401","owner":[],"postedDate":"September 20th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":37374198,"name":"Gastroenterology \u0026 Hepatology"},{"id":37374199,"name":"Internal Medicine"},{"id":37374200,"name":"Medical Informatics"},{"id":37374201,"name":"Information Retrieval and Management"},{"id":37374202,"name":"Gastrointestinal Surgery"}],"tags":[],"updatedAt":"2024-09-20T06:26:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-20 06:26:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5063235","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5063235","identity":"rs-5063235","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}