Report of radiological diagnosis and bioinformatics analysis of head and neck lymphoma

Report of radiological diagnosis and bioinformatics analysis of head and neck lymphoma | 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 Article Report of radiological diagnosis and bioinformatics analysis of head and neck lymphoma qili hu, honghua zhu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6634993/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 Malignant lymphomas that affect the head and neck region, including both Hodgkin's and non-Hodgkin's types, are serious cancers that pose significant challenges for early diagnosis. In our study, we employed bibliometric methods to analyze 5,253 scholarly articles from the Web of Science database, covering the years from 1978 to 2025, to better understand the current research landscape and future directions in this field. Our findings revealed that a substantial majority, specifically 68.91%, of these publications were produced in the last decade (2015-2025), indicating ongoing scientific interest in this area. Notable research institutions leading the way included Memorial Sloan Kettering Cancer Center, which published 95 articles, and Mayo Clinic with 85 articles. Among individual contributors, Albano D had 37 citations, while Meignan M stood out with 107 citations. Key journals that disseminated this research included well-established publications like Leukemia & Lymphoma, which featured 131 articles, as well as newer journals such as Cancers. Our term frequency analysis highlighted "prognostic factors" with 244 mentions and "positron emission tomography/computed tomography" with 225 mentions as the most frequently discussed concepts. Furthermore, the focus of contemporary research is shifting towards "radiomic analysis" and "metabolic tumor burden quantification." The integration of radiomic techniques with molecular signature profiling shows great potential for overcoming existing diagnostic challenges in this field. Biological sciences/Cancer Health sciences/Oncology head and neck lymphoma Bibliometric analysis PET-CT Radiomics Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Head and neck lymphomas encompass a wide range of cancers, including both Hodgkin's and non-Hodgkin's types, which pose significant clinical challenges. These cancers not only cause localized damage to tissues and impair function but also carry high mortality risks and create substantial financial burdens on healthcare systems. The complex anatomy of the head and neck region adds to the difficulties in diagnosis and treatment, making timely and accurate detection crucial for improving patient outcomes. Although traditional diagnostic methods mainly rely on CT and MRI scans, these techniques have limitations in distinguishing between different types of lymphoma and assessing the tumor's biological behavior, underscoring the need for more precise diagnostic tools. The emergence of multi-omics approaches, particularly radiomics and metabolomics, has revolutionized the diagnostic landscape for lymphoma. Advanced imaging technologies, such as PET/CT, offer improved capabilities for analyzing tumor metabolic activity, while innovative fields like radiogenomics are beginning to reveal the connections between imaging features and molecular characteristics. However, most current research tends to concentrate on evaluating single imaging modalities, lacking comprehensive comparisons among various diagnostic techniques. There are notable gaps in establishing standardized selection criteria, evaluating the relative effectiveness of different diagnostic methods, and integrating multi-modal data, which impede the clinical application of these advanced diagnostic strategies. Three critical research deficiencies necessitate immediate attention: first, there is a lack of comparative analyses among different imaging modalities for detecting lymphoma; second, the biological mechanisms that link imaging parameters with molecular indicators across various omics platforms remain poorly understood; and third, there is a shortage of large-scale, multi-institutional trials that validate the external applicability of existing diagnostic frameworks. These gaps not only hinder advancements in personalized medicine but also affect the development of clinical protocols, worsening geographical disparities in diagnostic accuracy, particularly in healthcare systems with limited resources. This study employs bibliometric techniques to thoroughly analyze 5,253 scientific articles indexed in the Web of Science over a span of 47 years, aiming to create a framework for the technological development of imaging-based diagnostic methods in cases of head and neck lymphoma. Unlike traditional literature reviews, bibliometric analysis offers a quantitative approach to evaluate research trends, identify key contributors and institutions, and reveal collaborative networks. By utilizing VOSviewer for co-occurrence network analysis and graphical representation, we investigate publication trends over time, inter-institutional collaboration, journal dissemination patterns, and the evolution of thematic keywords. This comprehensive analytical strategy provides significant empirical evidence for identifying emerging research areas and understanding developments within the discipline. The study aims to achieve four main goals: first, to outline the timeline of technological advancements in this field; second, to identify key research organizations and their collaborative networks; third, to assess how research priorities have evolved over time and what future directions they may take; and fourth, to provide evidence-based recommendations for clinical decision-making. The expected results could improve diagnostic procedures for lymphoma cases, encourage collaboration across different disciplines, and guide future research efforts, ultimately helping to incorporate precision medicine principles into strategies for treating lymphoma. Discussion Our comprehensive bibliometric evaluation of 5,253 scholarly articles published between 1978 and 2025 sheds light on the evolutionary patterns and current research frontiers in this scientific domain. The study identifies key research institutions and their collaborative networks, with Memorial Sloan Kettering Cancer Center and MD Anderson Cancer Center standing out as leading contributors. Notably, nearly 70% of the total publications emerged within the last ten years, highlighting the rapid advancements in this field. These findings provide a framework for assessing innovative technological approaches, such as radiomics and molecular biomarkers, while also offering valuable insights for enhancing diagnostic methodologies. The investigation reveals several important developments, with leading academic medical centers, particularly Memorial Sloan Kettering and MD Anderson, dominating both research output and collaborative networks, which reflects the significant resources needed for cutting-edge research. The fact that over two-thirds of publications were produced in the last decade, especially focusing on PET/CT imaging and radiomics applications, aligns with periods of technological breakthroughs. An analysis of authorship patterns indicates that consistent high-impact contributions from researchers like Michel Meignan have greatly influenced scholarly discussions, while frequently cited works by researchers such as Domenico Albano have directly shaped clinical practice guidelines for PET-CT applications. An analysis of journal distribution shows that Leukemia & Lymphoma continues to be a leading traditional publication, while Cancers has emerged as a prominent interdisciplinary platform, reflecting the field's shift from isolated technological studies to a more integrated approach to research. Keyword co-occurrence mapping reveals that "prognosis" is the most frequently used term, signaling a change in research focus from diagnostic methods to predictive outcome assessments. Innovative concepts like "radiomics" and "metabolic tumor volume" illustrate the transformative impact of artificial intelligence and quantitative analysis on conventional imaging techniques. The development of this field can be divided into three distinct periods. The first phase, from 1978 to 2000, was mainly centered on morphological assessments using CT and MRI technologies. The next phase, from 2001 to 2015, marked a shift towards functional imaging techniques, especially PET-CT. Currently, from 2016 to 2025, we are witnessing a convergence of multi-modal techniques and the application of artificial intelligence, which signifies a clinical shift from merely identifying structural features to evaluating functional aspects and creating personalized prognostic models. Notably, the combination of metabolomics biomarkers with radiomics parameters represents a groundbreaking advancement that could offer new insights into the analysis of lymphoma heterogeneity. Several critical considerations warrant attention regarding research productivity and its implications in the medical field. Geographically, there is a significant disparity, with institutions in North America and Western Europe contributing the majority of research output, while participation from Asia, excluding certain specialized centers, remains comparatively limited. This imbalance may be attributed to differences in research investment, technological infrastructure, and collaborative networks. Although the volume of publications has increased substantially in recent years, a thorough assessment of the substantive contributions and clinical relevance of these studies is necessary. Additionally, the limited citation impact of certain publications may indicate underlying issues related to research quality, innovation, or the effectiveness of dissemination. Future research directions should focus on implementing multi-institutional imaging repositories to address sampling limitations, formulating uniform radiomics analysis protocols, and designing clinically transparent artificial intelligence systems to enhance practical implementation. These developments are crucial for bridging the gap between scientific discovery and clinical application. Moreover, the challenges posed by head and neck lymphomas, which include both Hodgkin's and non-Hodgkin's variants, highlight significant diagnostic and therapeutic hurdles in clinical practice. These malignancies result in considerable tissue damage, functional deficits, and compromised patient outcomes, placing a substantial burden on healthcare systems. While conventional imaging modalities such as MRI and CT remain essential for diagnosis, their limitations in early tumor detection and accurate classification underscore the urgent need for improved diagnostic methodologies. This critical gap in clinical management drives the ongoing exploration of innovative diagnostic approaches and reinforces the importance of our research. Through comprehensive bibliometric evaluation of 5,253 scholarly articles spanning 1978-2025, we delineate the field's evolutionary patterns and current research priorities. Our analysis pinpoints preeminent research institutions (including Memorial Sloan Kettering Cancer Center) and influential investigators (such as Meignan M), while demonstrating that over two-thirds (68.91%) of publications emerged within the last ten years, indicating accelerated scientific progress in this area. These data provide a robust framework for assessing cutting-edge diagnostic technologies including radiomics and molecular biomarkers, while informing potential enhancements to clinical diagnostic algorithms. Key findings from our bibliometric investigation reveal several noteworthy patterns: Institutionally, premier cancer centers like Memorial Sloan Kettering and MD Anderson demonstrate exceptional research output and collaborative networks, underscoring the substantial resources required for impactful lymphoma research. The observation that nearly 70% of publications originated in the past decade—with particular emphasis on PET/CT applications and radiomics—correlates strongly with technological advancements in medical imaging. Analysis of author contributions indicates that consistent high-quality publications (exemplified by Meignan Michel's work) have significantly influenced academic dialogue, whereas frequently cited studies (such as those by Albano Domenico) have directly informed clinical PET-CT utilization guidelines. The analysis of publication trends reveals distinct patterns in journal preferences, with Leukemia & Lymphoma continuing to serve as a cornerstone publication venue, while Cancers demonstrates increasing influence as a newer interdisciplinary platform. This transition reflects the field's movement beyond isolated technological approaches toward more comprehensive methodologies. Through keyword frequency mapping, "prognosis" emerges as the predominant term, highlighting the growing emphasis on predictive capabilities rather than diagnostic applications alone. The appearance of novel concepts such as "radiomics" and "metabolic tumor volume" underscores the revolutionary effects of computational analytics and artificial intelligence on traditional imaging techniques. The historical development of this research domain can be categorized into three sequential eras: the foundational period (1978-2000) primarily concerned with structural evaluation using CT/MRI; the intermediate phase (2001-2015) marked by the ascendancy of PET-CT functional imaging; and the contemporary epoch (2016-2025) characterized by the convergence of multiple modalities and artificial intelligence applications. This trajectory parallels clinical practice's evolution from purely anatomical considerations to functional evaluation and individualized prognostic modeling. Particularly noteworthy is the synergistic combination of metabolomic indicators with radiomic parameters, representing a pioneering direction that may provide novel insights into lymphoma diversity. Significant disparities in research contributions are evident across different regions, with institutions in North America and Western Europe generating the majority of research output, while Asian participation, aside from a few specialized centers, remains relatively limited. Additionally, there is a notable implementation gap; for instance, only a small fraction of the 20 distinct proteins identified through proteomic studies have advanced to clinical evaluation. To address these issues, future initiatives should prioritize three main areas: first, the establishment of collaborative imaging repositories to reduce selection bias; second, the development of standardized protocols for radiomic analysis; and third, the creation of transparent artificial intelligence systems to enhance clinical utility. These advancements will be crucial for effectively bridging the gap between scientific discovery and its practical application. Data availability The datasets analyzed in this study are publicly available summary statistics. Data used can be obtained upon a reasonable request to the corresponding auithor. Materials and Methods 1.1 Data collection and retrieval strategy As one of the most extensive academic database access, Web of Science (https://www.webofscience.com/) includes a variety of high quality journals and comprehensive citation record. Among them, the most important database is the Web of Science Core Collection (WoSCC), which includes more than 12,000 academic journals in more than 250 disciplines. Therefore, this study used the Web of Science Core Collection (WoSCC) database to conduct a comprehensive bibliometric analysis. Bibliometric analysis is a systematic approach aimed at assessing, summarizing, and interpreting a large body of literature, providing insights into the current status of research, focus areas, and future trends in a specific field or topic. This analysis method combines the principles of statistics and informatics to quantify and evaluate literature resources and reveal the characteristics of a particular research field. In this study, the search strategy was used: [Topic (TS) = ("radiodiagnosis" OR "radiological diagnosis" OR "MRI" OR "Magnetic Resonance Imaging" OR "CT" OR "Computed Tomography") AND TS = ("Hodgkin Lymphoma" OR "Non-Hodgkin Lymphoma" OR "Diffuse Large B-cell Lymphoma" OR "Follicular Lymphoma" OR "Marginal Zone Lymphoma" OR "T-cell Lymphoma")]. 6883 records between 1978 and 2025 were identified, including all languages and document types, and the search was performed on March 25, 2025. The filtering of literature search results involved multiple stages: (1) only publications with document types of Article and Review Article were included; (2) only publications with English language type were included. A total of 5354 valid literature records were included after removing duplicates one by one. Finally, these literature records were saved in Plain Text File format for further quantitative and bibliometric analysis, including complete citation records such as title, key words, author information, abstract and references. 1.2 Analysis of annual publication trends in diagnostic radiology and head and neck lymphoma The number of publications over time indicates the trends and evolution of research in a particular field. Based on the literature data downloaded by WoSCC, we calculated the annual cumulative number of publications and annual number of publications in the fields of Radiological diagnosis and head and neck Lymphoma. The bar chart was drawn by ggplot2 [1] (Version 3.5.1) for visualization. 1.3 Analysis of institutional influence and cooperation Through the analysis of the number of publications and citation frequency of institutions, the important and active research institutions in the field of Radiological diagnosis and head and neck Lymphoma can be identified, and the research output, cooperative relationship and research focus of each institution in this field can be understood. To provide reference and guidance for further research cooperation, resource integration and field development. Based on the literature data downloaded from WoSCC, we used VOSviewer [2] (Version 1.6.20) to conduct a bibliometric analysis of institutions engaged in research related to Radiological diagnosis and head and neck Lymphoma. The method parameter was set as Association Strength, and the minimum number of publications threshold was 20. 1.4 Author influence and collaboration analysis By comprehensively analyzing the published works of numerous authors, a deeper understanding of outstanding researchers and fundamental research trends in the field can be obtained. Based on the literature data downloaded by WoSCC, we used VOSviewer (Version 1.6.20) to conduct a bibliometric analysis of authors engaged in research related to Radiological diagnosis and head and neck Lymphoma. The method parameter was set as Association Strength, and the minimum number of publications threshold was 10. 1.5 Analysis of core journals In order to understand which journals have high publication volume and influence in the field of Radiological diagnosis and head and neck Lymphoma research, based on the literature data downloaded by WoSCC, VOSviewer (Version 1.6.20) was used to conduct a bibliometric analysis of journals that published articles on Radiological diagnosis and head and neck Lymphoma, and a lollipop plot and time distribution map were drawn for visualization. The method parameter was set as Association Strength, and the minimum threshold of publication volume was 15. 1.6 Keyword analysis Keywords are highly condensed representations of the topics and core contents of literature research. When two or more keywords appear in the same literature, it is called keyword co-occurrence. The higher the co-occurrence frequency of keywords, the more relevant these keywords are. In order to identify research hotspots and future trends in the field of Radiological diagnosis and head and neck Lymphoma, based on the literature data downloaded by WoSCC, VOSviewer (Version 1.6.20) was used to conduct a bibliometric analysis of key words published on Radiological diagnosis and head and neck Lymphoma, and the time distribution map was drawn for visualization. The method parameter was set as Association Strength, and the minimum occurrence frequency threshold was 20. Results 2.1 Technology Roadmap See Fig 1 for details 2.2 Analysis of annual publication trend of radiological diagnosis and head and neck lymphoma A total of 5253 publications on the topic of Radiological diagnosis and head and neck Lymphoma were retrieved from 1978 to 2025, with a time span of 47 years. Fig2 shows the annual cumulative number of publications and annual number of publications in the field of Radiological diagnosis and head and neck Lymphoma. From 1978 to 2025, the annual cumulative number of articles published in this field increased steadily from 1 to 5253. The number of papers published in the past 10 years (2015-2025) accounted for 68.91% of all publications, indicating that researchers' attention and emphasis on Radiological diagnosis and head and neck Lymphoma continued to increase. 2.3 Analysis of institutional influence and cooperation In order to identify institutions with significant influence and activity in the fields of Radiological diagnosis and head and neck Lymphoma, VOSviewer was used to conduct a bibliometric analysis of institutions engaged in Radiological diagnosis and head and neck Lymphoma research. The results showed that 5632 institutions were involved in the research on Radiological diagnosis and head and neck Lymphoma, of which 84 institutions met the minimum threshold of publication volume. Table 1 and Fig3A show the top 10 institutions with the highest number of publications, mem sloan kettering canc ctr (N=95), MEM Sloan Kettering Canc CTR (n =95) and MEM Sloan Kettering Canc CTR (n =95) were the top three institutions for Radiological diagnosis and Lymphoma. mayo clin (N=85) and univ texas md anderson canc ctr (N=73). In addition, mem sloan kettering canc ctr (TLS=171), univ texas md anderson canc ctr (TLS=138), washington univ (TLS=104) leads the top 10 institutions in total link strength, indicating that these institutions maintain closer ties with other institutions in Radiological diagnosis and head and neck Lymphoma research (Fig3B). 2.4 Author influence and cooperation analysis To gain a deeper understanding of outstanding researchers and basic research trends in the fields of Radiological diagnosis and head and neck Lymphoma, we use VOSviewer engaged in diagnostic radiology (Radiological diagnosis) and head and neck Lymphoma (Lymphoma) study the author analyzed the literature metrology. Table 2 and Fig4A show the information of the Top10 authors, including their names, the number of publications, and the total number of citations of the papers. The results showed that a total of 28373 authors were involved in the research on Radiological diagnosis and head and neck Lymphoma, 136 authors reached the minimum publication threshold, and 135 authors had cooperative relationships. Among them, meignan and michel were the most prolific authors, with 107 papers published on Radiological diagnosis and head and neck Lymphoma, accounting for 2.03% of the total publications. albano, domenico was the most frequently cited author, with a total number of citations of 37. In addition, kostakoglu, lale exhibited the highest Link strength (Link=31), indicating its close collaboration with other authors on Radiological diagnosis and head and neck Lymphoma research (Fig4B). 2.5 Analysis of core journals In order to understand which journal have high publication volume and influence in the field of Radiological diagnosis and head and neck Lymphoma research, based on literature data downloaded by WoSCC, we used VOSviewer to conduct a bibliometric analysis of journals that published articles on Radiological diagnosis and head and neck Lymphoma. The results showed that a total of 1154 journals published articles on the topic of Radiological diagnosis and head and neck Lymphoma, of which 75 journals met the minimum publication threshold. Table 3 and Fig5A show the information of the journals with the Top10 publications. The journal with the largest number of publications was leukemia & lymphoma (N=131), followed by cureus journal of medical science (N=126). medicine (N=117). Different journals are colored according to their average year of appearance. The journals in the yellow node section are those with high publication volume and influence in the fields of Radiological diagnosis and head and neck Lymphoma research in recent years. Such as cancers and blood advance (Fig5B). 2.6 Keyword analysis Keywords are highly condensed representations of the topics and core contents of literature research. When two or more keywords appear in the same literature, it is called keyword co-occurrence. The higher the co-occurrence frequency of keywords, the more relevant these keywords are. In order to identify research hotspots and future trends in the field of Radiological diagnosis and head and neck Lymphoma, based on the literature data downloaded by WoSCC, A bibliometric analysis was conducted using VOSviewer (Version 1.6.20) to identify key words in published papers related to Radiological diagnosis and head and neck Lymphoma. The results showed that a total of 7585 key words appeared in papers related to Radiological diagnosis and head and neck Lymphoma, of which 99 reached the lowest frequency threshold. Table 4 and Fig6A show the information of keywords with the Top20 frequencies. The results showed that in addition to the search terms, "prognosis" (N=244) was the most frequently used search term, followed by "pet/ct" (N=225) and "positron emission tomography" (N=193). Different keywords were colored according to their average year of appearance. The keywords in the yellow node section were those with high frequency in the fields of Radiological diagnosis and head and neck Lymphoma research in recent years. Such as radiomics and metabolic tumor volume (Fig6B). References Wickham, H., ggplot2. Wiley interdisciplinary reviews: computational statistics, 2011. 3 (2): p. 180-185. Van Eck, N. and L. Waltman, Software survey: VOSviewer, a computer program for bibliometric mapping. scientometrics, 2010. 84 (2): p. 523-538. Hassan-Montero , Y., F. De-Moya-Anegon, and V.P. Guerrero-Bote, SCImago Graphica: a new tool for exploring and visually communicating data. Profesional de la informacion, 2022.31 (5). Tables Table 1 Top 10 institutions of Lymphoma and radiological diagnosis Rank Institution Paper Citation Total Link Strength 1 mem sloan kettering canc ctr 95 3826 171 2 mayo clin 85 1729 78 3 univ texas md anderson canc ctr 73 1901 138 4 sichuan univ 58 702 23 5 stanford univ 57 2201 81 6 zhejiang univ 55 535 13 7 washington univ 53 2243 104 8 sun yat sen univ 51 833 19 9 nanjing med univ 50 403 15 10 shanghai jiao tong univ 48 569 12 Table 2 Top 10 authors of Lymphoma and radiological diagnosis Rank Author Paper Citation Link 1 albano, domenico 98 37 8 2 nievelstein, rutger a. j. 101 34 7 3 kwee, thomas c. 103 32 8 4 meignan, michel 107 30 30 5 hutchings, martin 76 29 26 6 barrington, sally f. 51 27 23 7 kostakoglu, lale 76 27 31 8 gallamini, andrea 44 25 17 9 fijnheer, rob 92 23 7 10 tilly, herve 83 23 23 Table 3 Top 10 journal of Lymphoma and radiological diagnosis Rank Journal Paper Citation Total Link Strength 1 leukemia & lymphoma 131 2166 842 2 cureus journal of medical science 126 142 50 3 medicine 117 664 261 4 european journal of nuclear medicine and molecular imaging 76 3043 1305 5 internal medicine 72 437 55 6 journal of nuclear medicine 68 3125 999 7 annals of hematology 67 1311 520 8 frontiers in oncology 59 319 309 9 cancers 56 440 477 10 clinical lymphoma myeloma & leukemia 50 406 357 Table 4 The keywords with top 20 rankings in frequency Rank Keyword Paper Total Link Strength 1 lymphoma 867 1189 2 diffuse large b-cell lymphoma 469 627 3 hodgkin lymphoma 359 500 4 non-hodgkin lymphoma 349 505 5 ct 274 650 6 pet 246 589 7 prognosis 244 515 8 pet/ct 225 452 9 follicular lymphoma 201 289 10 positron emission tomography 193 388 11 computed tomography 186 335 12 case report 173 215 13 chemotherapy 170 257 14 magnetic resonance imaging 154 240 15 radiotherapy 124 185 16 rituximab 113 163 17 diffuse large b cell lymphoma 111 158 18 dlbcl 109 186 19 mri 109 214 20 non-hodgkin's lymphoma 107 137 Additional Declarations No competing interests reported. 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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-6634993","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":462755152,"identity":"bfdfbfda-577b-488d-93d4-70c919c14ad0","order_by":0,"name":"qili hu","email":"","orcid":"","institution":"The Department of Medical Imaging of the Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine.","correspondingAuthor":false,"prefix":"","firstName":"qili","middleName":"","lastName":"hu","suffix":""},{"id":462755153,"identity":"a57c1c8e-6dfb-4278-ac70-f8dcf8e8d195","order_by":1,"name":"honghua zhu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIie3PMQrCMBiG4V8K6fJD1xTEXiEiOAleJUHI5ObiIKUiOBVnQdErOHVOCXbKARzbI7hIJzHdpa2bQ17Ilod8AXC5/jFqDwcYgr9NFF8D9iYIqHNVmr4EGkLlIq/2PXZFp3RKy80MA1gyJS6xXajvtzYyOJsJ44XEMDGWZNoulPLRRjy6HJecaGR52hBlF+K0lRBq9/C3xrlVSpzjboLNK2Jv7xPClUi8bkKpXDFxkEiNpxQvNJKuv0THRRbWr9kouFa7Z72J54Gvi1by5Xe/XXe5XC7Xtz6DI0jUwvzn5QAAAABJRU5ErkJggg==","orcid":"","institution":"The Department of Medical Imaging of the Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine.","correspondingAuthor":true,"prefix":"","firstName":"honghua","middleName":"","lastName":"zhu","suffix":""}],"badges":[],"createdAt":"2025-05-10 13:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6634993/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6634993/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83681565,"identity":"ba4b06da-e5d8-485b-bdb4-d3d3f01c79b7","added_by":"auto","created_at":"2025-05-30 16:17:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":130062,"visible":true,"origin":"","legend":"\u003cp\u003eFlow Chart for the Comprehensive Analysis of Radiological diagnosis and Lymphoma\u003c/p\u003e\n\u003cp\u003eWoSCC, Web of Science Core Collection; TS, Topic Search; MRI, Magnetic Resonance Imaging; CT, Computed Tomography.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634993/v1/21ad99567cae2cbc8bb017eb.jpg"},{"id":83681567,"identity":"b37c047d-9eb5-4b07-adca-898f7912bdcc","added_by":"auto","created_at":"2025-05-30 16:17:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35086,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of annual publication trends in Radiological diagnosis and Lymphoma\u003c/p\u003e\n\u003cp\u003eThe annual cumulative number of publications (bar chart) and annual number of publications (dot plot) in the field of Radiological diagnosis and head and neck Lymphoma were analyzed.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634993/v1/3883e484624e6a955ec2cf77.jpg"},{"id":83681568,"identity":"05817f1a-1d5e-4526-a414-7a5d34ed9bf4","added_by":"auto","created_at":"2025-05-30 16:17:33","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":93402,"visible":true,"origin":"","legend":"\u003cp\u003eto analyze the influence and cooperation\u003c/p\u003e\n\u003cp\u003eA. Lollipop chart of the Top10 institutions in terms of publication volume. B. Cooperation network diagram of institutions with more than 20 publications. In the lollipop graph, the size of the circle is proportional to the number of publications, the redder the circle is, the higher the citation frequency is, and the lighter the circle is, the lower the citation frequency is. In the institutional cooperation network graph, nodes represent institutions, node size corresponds to the number of publications of each institution, node and line color represent the clustering relationship between these institutions, and the thickness of the line represents the cooperation strength between institutions.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634993/v1/c10b1e193bf0fe9bef00cd08.jpg"},{"id":83681566,"identity":"f49e98e4-20bb-4f38-8495-ebf4b2ee39bc","added_by":"auto","created_at":"2025-05-30 16:17:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":67875,"visible":true,"origin":"","legend":"\u003cp\u003eAnalysis of author influence and collaboration\u003c/p\u003e\n\u003cp\u003eA. Lollipop chart of the Top10 authors in terms of publication volume. B. Cooperative network of authors with more than 10 publications. In the lollipop graph, the size of the circle is proportional to the number of publications, the redder the circle is, the more frequently it is cited, and the lighter the circle is, the less frequently it is cited. In the author collaboration network diagram, the node said the author, the node size corresponding to each author number, node and the attachment color clustering relations between these authors, attachment cooperation intensity between the thickness of said the author.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634993/v1/db4ea8b2632fa1ecd2c90b4a.jpg"},{"id":83681569,"identity":"2cb5b52a-ced6-492a-973e-283d4a44d4a2","added_by":"auto","created_at":"2025-05-30 16:17:33","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":93856,"visible":true,"origin":"","legend":"\u003cp\u003eImpact analysis of journals\u003c/p\u003e\n\u003cp\u003eA. Lollipop graph of the Top10 journals by publication volume. B. Literature time distribution network diagram of journals with more than 15 publications. In the lollipop plot, the size of the circle is proportional to the number of articles published, the redder the circle color, the higher the citation frequency, and the lighter the circle color, the lower the citation frequency. In the network graph, the node size is proportional to the number of articles published, and the colors of nodes and lines represent different publication years.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634993/v1/12236dc99834e1dd9500a7f6.jpg"},{"id":83681570,"identity":"e4420f30-adb2-4078-b097-7739fca1749c","added_by":"auto","created_at":"2025-05-30 16:17:34","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":186584,"visible":true,"origin":"","legend":"\u003cp\u003eKeyword co-occurrence analysis\u003c/p\u003e\n\u003cp\u003eA-b. Thermodynamic map of keywords co-occurrence network (A) and time distribution network (B) with frequency \u0026gt;20 in the field of Radiological diagnosis and head and neck Lymphoma research. The color intensity in the thermodynamic map is directly related to the frequency of keywords. In the network diagram, the size of nodes is proportional to the frequency of occurrence, and the colors of nodes and lines represent different publication years.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6634993/v1/e6ffdcbc5d605b383d793aed.jpg"},{"id":98432181,"identity":"c8c8c88a-bd07-47ca-b4f0-0481b70ad4d2","added_by":"auto","created_at":"2025-12-17 16:49:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1271332,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6634993/v1/735cee4d-2a0f-452d-907d-7ab65928318e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Report of radiological diagnosis and bioinformatics analysis of head and neck lymphoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHead and neck lymphomas encompass a wide range of cancers, including both Hodgkin's and non-Hodgkin's types, which pose significant clinical challenges. These cancers not only cause localized damage to tissues and impair function but also carry high mortality risks and create substantial financial burdens on healthcare systems. The complex anatomy of the head and neck region adds to the difficulties in diagnosis and treatment, making timely and accurate detection crucial for improving patient outcomes. Although traditional diagnostic methods mainly rely on CT and MRI scans, these techniques have limitations in distinguishing between different types of lymphoma and assessing the tumor's biological behavior, underscoring the need for more precise diagnostic tools. The emergence of multi-omics approaches, particularly radiomics and metabolomics, has revolutionized the diagnostic landscape for lymphoma. Advanced imaging technologies, such as PET/CT, offer improved capabilities for analyzing tumor metabolic activity, while innovative fields like radiogenomics are beginning to reveal the connections between imaging features and molecular characteristics. However, most current research tends to concentrate on evaluating single imaging modalities, lacking comprehensive comparisons among various diagnostic techniques. There are notable gaps in establishing standardized selection criteria, evaluating the relative effectiveness of different diagnostic methods, and integrating multi-modal data, which impede the clinical application of these advanced diagnostic strategies.\u003c/p\u003e\n\u003cp\u003eThree critical research deficiencies necessitate immediate attention: first, there is a lack of comparative analyses among different imaging modalities for detecting lymphoma; second, the biological mechanisms that link imaging parameters with molecular indicators across various omics platforms remain poorly understood; and third, there is a shortage of large-scale, multi-institutional trials that validate the external applicability of existing diagnostic frameworks. These gaps not only hinder advancements in personalized medicine but also affect the development of clinical protocols, worsening geographical disparities in diagnostic accuracy, particularly in healthcare systems with limited resources. This study employs bibliometric techniques to thoroughly analyze 5,253 scientific articles indexed in the Web of Science over a span of 47 years, aiming to create a framework for the technological development of imaging-based diagnostic methods in cases of head and neck lymphoma. Unlike traditional literature reviews, bibliometric analysis offers a quantitative approach to evaluate research trends, identify key contributors and institutions, and reveal collaborative networks. By utilizing VOSviewer for co-occurrence network analysis and graphical representation, we investigate publication trends over time, inter-institutional collaboration, journal dissemination patterns, and the evolution of thematic keywords. This comprehensive analytical strategy provides significant empirical evidence for identifying emerging research areas and understanding developments within the discipline.\u003c/p\u003e\n\u003cp\u003eThe study aims to achieve four main goals: first, to outline the timeline of technological advancements in this field; second, to identify key research organizations and their collaborative networks; third, to assess how research priorities have evolved over time and what future directions they may take; and fourth, to provide evidence-based recommendations for clinical decision-making. The expected results could improve diagnostic procedures for lymphoma cases, encourage collaboration across different disciplines, and guide future research efforts, ultimately helping to incorporate precision medicine principles into strategies for treating lymphoma.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur comprehensive bibliometric evaluation of 5,253 scholarly articles published between 1978 and 2025 sheds light on the evolutionary patterns and current research frontiers in this scientific domain. The study identifies key research institutions and their collaborative networks, with Memorial Sloan Kettering Cancer Center and MD Anderson Cancer Center standing out as leading contributors. Notably, nearly 70% of the total publications emerged within the last ten years, highlighting the rapid advancements in this field. These findings provide a framework for assessing innovative technological approaches, such as radiomics and molecular biomarkers, while also offering valuable insights for enhancing diagnostic methodologies. The investigation reveals several important developments, with leading academic medical centers, particularly Memorial Sloan Kettering and MD Anderson, dominating both research output and collaborative networks, which reflects the significant resources needed for cutting-edge research. The fact that over two-thirds of publications were produced in the last decade, especially focusing on PET/CT imaging and radiomics applications, aligns with periods of technological breakthroughs. An analysis of authorship patterns indicates that consistent high-impact contributions from researchers like Michel Meignan have greatly influenced scholarly discussions, while frequently cited works by researchers such as Domenico Albano have directly shaped clinical practice guidelines for PET-CT applications.\u003c/p\u003e\n\u003cp\u003eAn analysis of journal distribution shows that Leukemia \u0026amp; Lymphoma continues to be a leading traditional publication, while Cancers has emerged as a prominent interdisciplinary platform, reflecting the field's shift from isolated technological studies to a more integrated approach to research. Keyword co-occurrence mapping reveals that \"prognosis\" is the most frequently used term, signaling a change in research focus from diagnostic methods to predictive outcome assessments. Innovative concepts like \"radiomics\" and \"metabolic tumor volume\" illustrate the transformative impact of artificial intelligence and quantitative analysis on conventional imaging techniques. The development of this field can be divided into three distinct periods. The first phase, from 1978 to 2000, was mainly centered on morphological assessments using CT and MRI technologies. The next phase, from 2001 to 2015, marked a shift towards functional imaging techniques, especially PET-CT. Currently, from 2016 to 2025, we are witnessing a convergence of multi-modal techniques and the application of artificial intelligence, which signifies a clinical shift from merely identifying structural features to evaluating functional aspects and creating personalized prognostic models. Notably, the combination of metabolomics biomarkers with radiomics parameters represents a groundbreaking advancement that could offer new insights into the analysis of lymphoma heterogeneity.\u003c/p\u003e\n\u003cp\u003eSeveral critical considerations warrant attention regarding research productivity and its implications in the medical field. Geographically, there is a significant disparity, with institutions in North America and Western Europe contributing the majority of research output, while participation from Asia, excluding certain specialized centers, remains comparatively limited. This imbalance may be attributed to differences in research investment, technological infrastructure, and collaborative networks. Although the volume of publications has increased substantially in recent years, a thorough assessment of the substantive contributions and clinical relevance of these studies is necessary. Additionally, the limited citation impact of certain publications may indicate underlying issues related to research quality, innovation, or the effectiveness of dissemination. Future research directions should focus on implementing multi-institutional imaging repositories to address sampling limitations, formulating uniform radiomics analysis protocols, and designing clinically transparent artificial intelligence systems to enhance practical implementation. These developments are crucial for bridging the gap between scientific discovery and clinical application. Moreover, the challenges posed by head and neck lymphomas, which include both Hodgkin's and non-Hodgkin's variants, highlight significant diagnostic and therapeutic hurdles in clinical practice. These malignancies result in considerable tissue damage, functional deficits, and compromised patient outcomes, placing a substantial burden on healthcare systems. While conventional imaging modalities such as MRI and CT remain essential for diagnosis, their limitations in early tumor detection and accurate classification underscore the urgent need for improved diagnostic methodologies. This critical gap in clinical management drives the ongoing exploration of innovative diagnostic approaches and reinforces the importance of our research.\u003c/p\u003e\n\u003cp\u003eThrough comprehensive bibliometric evaluation of 5,253 scholarly articles spanning 1978-2025, we delineate the field's evolutionary patterns and current research priorities. Our analysis pinpoints preeminent research institutions (including Memorial Sloan Kettering Cancer Center) and influential investigators (such as Meignan M), while demonstrating that over two-thirds (68.91%) of publications emerged within the last ten years, indicating accelerated scientific progress in this area. These data provide a robust framework for assessing cutting-edge diagnostic technologies including radiomics and molecular biomarkers, while informing potential enhancements to clinical diagnostic algorithms.\u003c/p\u003e\n\u003cp\u003eKey findings from our bibliometric investigation reveal several noteworthy patterns: Institutionally, premier cancer centers like Memorial Sloan Kettering and MD Anderson demonstrate exceptional research output and collaborative networks, underscoring the substantial resources required for impactful lymphoma research. The observation that nearly 70% of publications originated in the past decade—with particular emphasis on PET/CT applications and radiomics—correlates strongly with technological advancements in medical imaging. Analysis of author contributions indicates that consistent high-quality publications (exemplified by Meignan Michel's work) have significantly influenced academic dialogue, whereas frequently cited studies (such as those by Albano Domenico) have directly informed clinical PET-CT utilization guidelines.\u003c/p\u003e\n\u003cp\u003eThe analysis of publication trends reveals distinct patterns in journal preferences, with Leukemia \u0026amp; Lymphoma continuing to serve as a cornerstone publication venue, while Cancers demonstrates increasing influence as a newer interdisciplinary platform. This transition reflects the field's movement beyond isolated technological approaches toward more comprehensive methodologies. Through keyword frequency mapping, \"prognosis\" emerges as the predominant term, highlighting the growing emphasis on predictive capabilities rather than diagnostic applications alone. The appearance of novel concepts such as \"radiomics\" and \"metabolic tumor volume\" underscores the revolutionary effects of computational analytics and artificial intelligence on traditional imaging techniques.\u003c/p\u003e\n\u003cp\u003eThe historical development of this research domain can be categorized into three sequential eras: the foundational period (1978-2000) primarily concerned with structural evaluation using CT/MRI; the intermediate phase (2001-2015) marked by the ascendancy of PET-CT functional imaging; and the contemporary epoch (2016-2025) characterized by the convergence of multiple modalities and artificial intelligence applications. This trajectory parallels clinical practice's evolution from purely anatomical considerations to functional evaluation and individualized prognostic modeling. Particularly noteworthy is the synergistic combination of metabolomic indicators with radiomic parameters, representing a pioneering direction that may provide novel insights into lymphoma diversity.\u003c/p\u003e\n\u003cp\u003eSignificant disparities in research contributions are evident across different regions, with institutions in North America and Western Europe generating the majority of research output, while Asian participation, aside from a few specialized centers, remains relatively limited. Additionally, there is a notable implementation gap; for instance, only a small fraction of the 20 distinct proteins identified through proteomic studies have advanced to clinical evaluation. To address these issues, future initiatives should prioritize three main areas: first, the establishment of collaborative imaging repositories to reduce selection bias; second, the development of standardized protocols for radiomic analysis; and third, the creation of transparent artificial intelligence systems to enhance clinical utility. These advancements will be crucial for effectively bridging the gap between scientific discovery and its practical application.\u003c/p\u003e\n\u003cp\u003eData availability\u003c/p\u003e\n\u003cp\u003eThe datasets analyzed in this study are publicly available summary statistics. Data used can be obtained upon a reasonable request to the corresponding auithor.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003ch3\u003e1.1 Data collection and retrieval strategy\u003c/h3\u003e\n\u003cp\u003eAs one of the most extensive academic database access, Web of Science (https://www.webofscience.com/) includes a variety of high quality journals and comprehensive citation record. Among them, the most important database is the Web of Science Core Collection (WoSCC), which includes more than 12,000 academic journals in more than 250 disciplines. Therefore, this study used the Web of Science Core Collection (WoSCC) database to conduct a comprehensive bibliometric analysis. Bibliometric analysis is a systematic approach aimed at assessing, summarizing, and interpreting a large body of literature, providing insights into the current status of research, focus areas, and future trends in a specific field or topic. This analysis method combines the principles of statistics and informatics to quantify and evaluate literature resources and reveal the characteristics of a particular research field.\u003c/p\u003e\n\u003cp\u003eIn this study, the search strategy was used: [Topic (TS) = (\"radiodiagnosis\" OR \"radiological diagnosis\" OR \"MRI\" OR \"Magnetic Resonance Imaging\" OR \"CT\" OR \"Computed Tomography\") AND TS = (\"Hodgkin Lymphoma\" OR \"Non-Hodgkin Lymphoma\" OR \"Diffuse Large B-cell Lymphoma\" OR \"Follicular Lymphoma\" OR \"Marginal Zone Lymphoma\" OR \"T-cell Lymphoma\")]. 6883 records between 1978 and 2025 were identified, including all languages and document types, and the search was performed on March 25, 2025.\u0026nbsp;The filtering of literature search results involved multiple stages: (1) only publications with document types of Article and Review Article were included; (2) only publications with English language type were included. A total of 5354 valid literature records were included after removing duplicates one by one. Finally, these literature records were saved in Plain Text File format for further quantitative and bibliometric analysis, including complete citation records such as title, key words, author information, abstract and references.\u003c/p\u003e\n\u003ch3\u003e1.2 Analysis of annual publication trends in diagnostic radiology and head and neck lymphoma\u003c/h3\u003e\n\u003cp\u003eThe number of publications over time indicates the trends and evolution of research in a particular field. Based on the literature data downloaded by WoSCC, we calculated the annual cumulative number of publications and annual number of publications in the fields of Radiological diagnosis and head and neck Lymphoma. The bar chart was drawn by ggplot2\u003csup\u003e[1]\u003c/sup\u003e (Version 3.5.1) for visualization.\u003c/p\u003e\n\u003ch3\u003e1.3 Analysis of institutional influence and cooperation\u003c/h3\u003e\n\u003cp\u003eThrough the analysis of the number of publications and citation frequency of institutions, the important and active research institutions in the field of Radiological diagnosis and head and neck Lymphoma can be identified, and the research output, cooperative relationship and research focus of each institution in this field can be understood. To provide reference and guidance for further research cooperation, resource integration and field development. Based on the literature data downloaded from WoSCC, we used VOSviewer\u003csup\u003e[2]\u003c/sup\u003e (Version 1.6.20) to conduct a bibliometric analysis of institutions engaged in research related to Radiological diagnosis and head and neck Lymphoma. The method parameter was set as Association Strength, and the minimum number of publications threshold was 20.\u003c/p\u003e\n\u003ch3\u003e1.4 Author influence and collaboration analysis\u003c/h3\u003e\n\u003cp\u003eBy comprehensively analyzing the published works of numerous authors, a deeper understanding of outstanding researchers and fundamental research trends in the field can be obtained. Based on the literature data downloaded by WoSCC, we used VOSviewer (Version 1.6.20) to conduct a bibliometric analysis of authors engaged in research related to Radiological diagnosis and head and neck Lymphoma. The method parameter was set as Association Strength, and the minimum number of publications threshold was 10.\u003c/p\u003e\n\u003ch3\u003e1.5 Analysis of core journals\u003c/h3\u003e\n\u003cp\u003eIn order to understand which journals have high publication volume and influence in the field of Radiological diagnosis and head and neck Lymphoma research, based on the literature data downloaded by WoSCC, VOSviewer (Version 1.6.20) was used to conduct a bibliometric analysis of journals that published articles on Radiological diagnosis and head and neck Lymphoma, and a lollipop plot and time distribution map were drawn for visualization. The method parameter was set as Association Strength, and the minimum threshold of publication volume was 15.\u003c/p\u003e\n\u003ch3\u003e1.6 Keyword analysis\u003c/h3\u003e\n\u003cp\u003eKeywords are highly condensed representations of the topics and core contents of literature research. When two or more keywords appear in the same literature, it is called keyword co-occurrence. The higher the co-occurrence frequency of keywords, the more relevant these keywords are. In order to identify research hotspots and future trends in the field of Radiological diagnosis and head and neck Lymphoma, based on the literature data downloaded by WoSCC, VOSviewer (Version 1.6.20) was used to conduct a bibliometric analysis of key words published on Radiological diagnosis and head and neck Lymphoma, and the time distribution map was drawn for visualization. The method parameter was set as Association Strength, and the minimum occurrence frequency threshold was 20.\u003c/p\u003e"},{"header":"Results","content":"\u003ch3\u003e2.1 Technology Roadmap\u003c/h3\u003e\n\u003cp\u003eSee Fig 1 for details\u003c/p\u003e\n\u003ch3\u003e2.2 Analysis of annual publication trend of radiological diagnosis and head and neck lymphoma\u003c/h3\u003e\n\u003cp\u003eA total of 5253 publications on the topic of Radiological diagnosis and head and neck Lymphoma were retrieved from 1978 to 2025, with a time span of 47 years. Fig2 shows the annual cumulative number of publications and annual number of publications in the field of Radiological diagnosis and head and neck Lymphoma. From 1978 to 2025, the annual cumulative number of articles published in this field increased steadily from 1 to 5253. The number of papers published in the past 10 years (2015-2025) accounted for 68.91% of all publications, indicating that researchers\u0026apos; attention and emphasis on Radiological diagnosis and head and neck Lymphoma continued to increase.\u003c/p\u003e\n\u003ch3\u003e2.3 Analysis of institutional influence and cooperation\u003c/h3\u003e\n\u003cp\u003eIn order to identify institutions with significant influence and activity in the fields of Radiological diagnosis and head and neck Lymphoma, VOSviewer was used to conduct a bibliometric analysis of institutions engaged in Radiological diagnosis and head and neck Lymphoma research. The results showed that 5632 institutions were involved in the research on Radiological diagnosis and head and neck Lymphoma, of which 84 institutions met the minimum threshold of publication volume. Table 1 and Fig3A show the top 10 institutions with the highest number of publications, mem sloan kettering canc ctr (N=95), MEM Sloan Kettering Canc CTR (n =95) and MEM Sloan Kettering Canc CTR (n =95) were the top three institutions for Radiological diagnosis and Lymphoma. mayo clin (N=85) and univ texas md anderson canc ctr (N=73). In addition, mem sloan kettering canc ctr (TLS=171), univ texas md anderson canc ctr (TLS=138), washington univ (TLS=104) leads the top 10 institutions in total link strength, indicating that these institutions maintain closer ties with other institutions in Radiological diagnosis and head and neck Lymphoma research (Fig3B).\u003c/p\u003e\n\u003ch3\u003e2.4 Author influence and cooperation analysis\u003c/h3\u003e\n\u003cp\u003eTo gain a deeper understanding of outstanding researchers and basic research trends in the fields of Radiological diagnosis and head and neck Lymphoma, we use VOSviewer engaged in diagnostic radiology (Radiological diagnosis) and head and neck Lymphoma (Lymphoma) study the author analyzed the literature metrology. Table 2 and Fig4A show the information of the Top10 authors, including their names, the number of publications, and the total number of citations of the papers. The results showed that a total of 28373 authors were involved in the research on Radiological diagnosis and head and neck Lymphoma, 136 authors reached the minimum publication threshold, and 135 authors had cooperative relationships. Among them, meignan and michel were the most prolific authors, with 107 papers published on Radiological diagnosis and head and neck Lymphoma, accounting for 2.03% of the total publications. albano, domenico was the most frequently cited author, with a total number of citations of 37. In addition, kostakoglu, lale exhibited the highest Link strength (Link=31), indicating its close collaboration with other authors on Radiological diagnosis and head and neck Lymphoma research (Fig4B).\u003c/p\u003e\n\u003ch3\u003e2.5 Analysis of core journals\u003c/h3\u003e\n\u003cp\u003eIn order to understand which journal have high publication volume and influence in the field of Radiological diagnosis and head and neck Lymphoma research, based on literature data downloaded by WoSCC, we used VOSviewer to conduct a bibliometric analysis of journals that published articles on Radiological diagnosis and head and neck Lymphoma. The results showed that a total of 1154 journals published articles on the topic of Radiological diagnosis and head and neck Lymphoma, of which 75 journals met the minimum publication threshold. Table 3 and Fig5A show the information of the journals with the Top10 publications. The journal with the largest number of publications was leukemia \u0026amp; lymphoma (N=131), followed by cureus journal of medical science (N=126). medicine (N=117). Different journals are colored according to their average year of appearance. The journals in the yellow node section are those with high publication volume and influence in the fields of Radiological diagnosis and head and neck Lymphoma research in recent years. Such as cancers and blood advance (Fig5B).\u003c/p\u003e\n\u003ch3\u003e2.6 Keyword analysis\u003c/h3\u003e\n\u003cp\u003eKeywords are highly condensed representations of the topics and core contents of literature research. When two or more keywords appear in the same literature, it is called keyword co-occurrence. The higher the co-occurrence frequency of keywords, the more relevant these keywords are. In order to identify research hotspots and future trends in the field of Radiological diagnosis and head and neck Lymphoma, based on the literature data downloaded by WoSCC, A bibliometric analysis was conducted using VOSviewer (Version 1.6.20) to identify key words in published papers related to Radiological diagnosis and head and neck Lymphoma. The results showed that a total of 7585 key words appeared in papers related to Radiological diagnosis and head and neck Lymphoma, of which 99 reached the lowest frequency threshold. Table 4 and Fig6A show the information of keywords with the Top20 frequencies. The results showed that in addition to the search terms, \u0026quot;prognosis\u0026quot; (N=244) was the most frequently used search term, followed by \u0026quot;pet/ct\u0026quot; (N=225) and \u0026quot;positron emission tomography\u0026quot; (N=193). Different keywords were colored according to their average year of appearance. The keywords in the yellow node section were those with high frequency in the fields of Radiological diagnosis and head and neck Lymphoma research in recent years. Such as radiomics and metabolic tumor volume (Fig6B).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWickham, H., \u003cem\u003eggplot2.\u003c/em\u003e Wiley interdisciplinary reviews: computational statistics, 2011. \u003cstrong\u003e3\u003c/strong\u003e(2): p. 180-185.\u003c/li\u003e\n\u003cli\u003eVan Eck, N. and L. Waltman, \u003cem\u003eSoftware survey: VOSviewer, a computer program for bibliometric mapping.\u003c/em\u003e scientometrics, 2010. \u003cstrong\u003e84\u003c/strong\u003e(2): p. 523-538.\u003c/li\u003e\n\u003cli\u003eHassan-Montero , Y., F. De-Moya-Anegon, and V.P. Guerrero-Bote, \u003cem\u003eSCImago Graphica: a new tool for exploring and visually communicating data.\u003c/em\u003e Profesional de la informacion, \u003cstrong\u003e2022.31\u003c/strong\u003e (5).\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 Top 10 institutions of\u0026nbsp;Lymphoma and radiological diagnosis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003eInstitution\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003ePaper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003eCitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003eTotal Link Strength\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003emem sloan kettering canc ctr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e3826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003emayo clin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e1729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003euniv texas md anderson canc ctr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e1901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003esichuan univ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e702\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003estanford univ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e2201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003ezhejiang univ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e535\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003ewashington univ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e2243\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003esun yat sen univ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003enanjing med univ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e403\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.56102%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 35.8834%;\"\u003e\n \u003cp\u003eshanghai jiao tong univ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.7468%;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.4827%;\"\u003e\n \u003cp\u003e569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.326%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 2 Top 10 authors of\u0026nbsp;Lymphoma and radiological diagnosis\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"548\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003eAuthor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003ePaper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003eCitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003eLink\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003ealbano, domenico\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003enievelstein, rutger a. j.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e101\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003ekwee, thomas c.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e103\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003emeignan, michel\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003ehutchings, martin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003ebarrington, sally f.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003ekostakoglu, lale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003egallamini, andrea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003efijnheer, rob\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 10.4015%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 37.5912%;\"\u003e\n \u003cp\u003etilly, herve\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 7.66423%;\"\u003e\n \u003cp\u003e83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.3285%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0146%;\"\u003e\n \u003cp\u003e23\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eTable 3 Top 10 journal of\u0026nbsp;Lymphoma and radiological diagnosis\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"821\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003eJournal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003ePaper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003eCitation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003eTotal Link Strength\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003eleukemia \u0026amp; lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e2166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e842\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003ecureus journal of medical science\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e126\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e142\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003emedicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e261\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003eeuropean journal of nuclear medicine and molecular imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e3043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e1305\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003einternal medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e437\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003ejournal of nuclear medicine\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e3125\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e999\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003eannals of hematology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e1311\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e520\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003efrontiers in oncology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e309\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003ecancers\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e477\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 17.8049%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 45.3659%;\"\u003e\n \u003cp\u003eclinical lymphoma myeloma \u0026amp; leukemia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4878%;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.3659%;\"\u003e\n \u003cp\u003e406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.9756%;\"\u003e\n \u003cp\u003e357\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003cp\u003eTable 4 The keywords with top 20 rankings in frequency\u0026nbsp;\u003c/p\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"747\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003eRank\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003eKeyword\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003ePaper\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003eTotal Link Strength\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003elymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e1189\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003ediffuse large b-cell lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003ehodgkin lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e500\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003enon-hodgkin lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e505\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003ect\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e650\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003epet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e589\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003eprognosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e515\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003epet/ct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e452\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003efollicular lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e289\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003epositron emission tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e193\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e388\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003ecomputed tomography\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e335\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003ecase report\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e215\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003echemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003emagnetic resonance imaging\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e240\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003eradiotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e185\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003erituximab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e163\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003ediffuse large b cell lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e111\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e158\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003edlbcl\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003emri\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e214\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 12.7175%;\"\u003e\n \u003cp\u003e20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 27.8447%;\"\u003e\n \u003cp\u003enon-hodgkin\u0026apos;s lymphoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 30.3882%;\"\u003e\n \u003cp\u003e107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 29.0495%;\"\u003e\n \u003cp\u003e137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","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":"head and neck lymphoma, Bibliometric analysis, PET-CT, Radiomics","lastPublishedDoi":"10.21203/rs.3.rs-6634993/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6634993/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eMalignant lymphomas that affect the head and neck region, including both Hodgkin's and non-Hodgkin's types, are serious cancers that pose significant challenges for early diagnosis. In our study, we employed bibliometric methods to analyze 5,253 scholarly articles from the Web of Science database, covering the years from 1978 to 2025, to better understand the current research landscape and future directions in this field. Our findings revealed that a substantial majority, specifically 68.91%, of these publications were produced in the last decade (2015-2025), indicating ongoing scientific interest in this area. Notable research institutions leading the way included Memorial Sloan Kettering Cancer Center, which published 95 articles, and Mayo Clinic with 85 articles. Among individual contributors, Albano D had 37 citations, while Meignan M stood out with 107 citations. Key journals that disseminated this research included well-established publications like Leukemia \u0026amp; Lymphoma, which featured 131 articles, as well as newer journals such as Cancers. Our term frequency analysis highlighted \"prognostic factors\" with 244 mentions and \"positron emission tomography/computed tomography\" with 225 mentions as the most frequently discussed concepts. Furthermore, the focus of contemporary research is shifting towards \"radiomic analysis\" and \"metabolic tumor burden quantification.\" The integration of radiomic techniques with molecular signature profiling shows great potential for overcoming existing diagnostic challenges in this field.\u003c/p\u003e","manuscriptTitle":"Report of radiological diagnosis and bioinformatics analysis of head and neck lymphoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 16:17:29","doi":"10.21203/rs.3.rs-6634993/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":"fb91407b-fb6b-4199-9141-6e9a6b8c1d37","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":49136016,"name":"Biological sciences/Cancer"},{"id":49136017,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-12-15T06:09:51+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-30 16:17:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6634993","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6634993","identity":"rs-6634993","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}