Mapping the knowledge landscape of lymph node metastasis in ovarian cancer: a bibliometric and collaboration network analysis

Introduction

According to GLOBOCAN 2022 data, approximately 325,000 new ovarian cancer cases were diagnosed worldwide in 2022, with about 207,000 deaths. The disease ranks 18th globally in terms of incidence and 8th among female malignancies (1). Among gynecological cancers, ovarian cancer has the third highest incidence rate. However, its mortality rate remains high, making it the leading cause of gynecologic cancer-related deaths in Western countries. This is largely due to the insidious onset of early symptoms and the absence of effective screening methods, leading to the diagnosis of approximately 70% of cases at advanced stages (FIGO III–IV) (2). This underscores the serious threat ovarian cancer poses to women’s health and quality of life.

Lymphatic metastasis is one of the most common routes for the dissemination of ovarian cancer. Studies suggest that 20–40% of ovarian cancer patients exhibit lymph node involvement, with the percentage rising to 50–75% in advanced-stage cases (3). The primary sites of lymphatic metastasis are the pelvic and para-aortic retroperitoneal lymph nodes (4). Lymph node metastasis (LNM) significantly impacts disease staging and prognosis; involvement of any regional lymph nodes elevates the FIGO stage to at least stage III (2). Lymph node positivity is widely recognized as an unfavorable prognostic factor, typically predicting shorter overall survival (5). Therefore, during early surgery, routine sampling or dissection of pelvic and paraaortic lymph nodes is performed to detect potential metastases and accurately assess the extent of the disease (6). If preoperative imaging or intraoperative exploration reveals LNM, removal of affected nodes is generally recommended to achieve complete tumor clearance and potentially improve patient survival (7). In summary, evaluating lymphatic metastasis is clinically essential for accurate diagnosis, staging, treatment decisions, and prognosis in ovarian cancer management.

Bibliometrics uses mathematical and statistical methods to quantitatively evaluate the development of a scientific discipline. A knowledge graph, as a derivative tool, visually presents the structural characteristics of scientific research, capturing emerging trends and identifying research hotspots (8). This method offers valuable direction for future studies (9).

The research field of lymphatic metastasis in ovarian cancer has generated a vast and rapidly expanding volume of literature, making it challenging to comprehensively synthesize its intellectual structure and evolving frontiers through traditional reviews. A bibliometric analysis is therefore necessary to provide a data-driven, objective overview of the field. It can map the extensive interdisciplinary collaboration (spanning oncology, surgery, pathology, and molecular biology) and cross-regional research efforts, identify core research themes and gaps, and inform strategic directions for future research and resource allocation (10). To address this need, this study applied bibliometric methods to systematically examine academic outputs from 2010 to 2025, with a focus on identifying core contributors, evaluating current trends, and predicting future directions. We present this article in accordance with the BIBLIO reporting checklist (available at https://gpm.amegroups.com/article/view/10.21037/gpm-25-47/rc).

Methods

Data sources

On June 10, 2025, a systematic literature search was conducted in the Web of Science Core Collection (WoSCC), WoSCC was chosen as the sole data source for this study, as it encompasses a wide range of biomedical journals and offers precise bibliometric parameters, such as citation counts, keywords, content summaries, and reference lists. Studies were selected based on relevance to LNM of ovarian cancer (Table 1). The specific screening methods and inclusion criteria are outlined in Figure 1, which includes the publication screening flowchart. The advanced search function of the WoSCC database was used, with the search formula “TS= (Ovarian Neoplasms OR Cancer of Ovary OR Ovarian Cancer OR Ovary Cancer OR Ovary Neoplasms OR Ovarian Carcinoma OR Ovarian Tumor OR Ovarian Malignan) AND (Lymph Node OR Lymphatic Node OR Lymphatic) AND (Metastas OR Metastatic)”, restricting the publication years to 2010–2025 and limiting document types to English-language research articles and reviews. Retrieved records were exported as “full records and cited references”, then converted into plain-text files and saved in a downloadable format (“.txt”). A total of 845 records were retrieved, consisting of 789 articles (93.4%) and 56 reviews (6.6%). Each author actively participated in data collection, screening, and manuscript drafting.

Figure 1 Publications screening flowchart. LN, lymph node.

Research tools

This study employed several software tools to conduct bibliometric analysis and data visualization. Data processing and analysis were performed using Microsoft Excel 2019, VOSviewer 1.6.20, CiteSpace 6.1.R1, Gephi 0.9.2, OriginPro 2024, and the Bibliometrix and ggplot2 packages in R. The online platform Charticulator was used to assist with drawing charts. For exploring the cooperation network between countries and institutions, we first used VOSviewer to preprocess the raw data. The results were then imported into Gephi to construct a node-edge table, and Charticulator was used to refine the chord graph.

VOSviewer (version 1.6.20) is a bibliometric software tool designed to extract and analyze key information from large volumes of publications (11). It is commonly used for constructing collaboration, co-citation, and co-occurrence networks (12,13). In this study, VOSviewer was primarily used for the analysis of country and institution collaborations, journal and co-cited journal analysis, author and co-cited author analysis, and keyword co-occurrence analysis. In the generated maps, each node represents an item (e.g., country, institution, journal, or author). The size and color of the nodes reflect the number and classification of items, while the line thickness between nodes indicates the degree of collaboration or co-citation (14,15).

CiteSpace (version 6.1.R1), developed by Professor Chaomei Chen, was used for bibliometric analysis (13,16). This tool was utilized to draw journal overlay maps and conduct burst detection on highly cited literature.

Additionally, the R package “bibliometrix” (version 3.2.1; https://www.bibliometrix.org) was applied to conduct thematic evolution analysis and to construct a global network of publications related to ovarian cancer lymphatic metastasis (17). Data from the 2020 Journal Citation Reports (quartile rankings and impact factors) were incorporated into our quantitative analysis, with calculations performed in Excel 2019.

Statistical analysis

Descriptive bibliometric methods were used to summarize publication characteristics, including annual publication output, document types, countries, institutions, authors, journals, citations, and keywords. Publication counts, percentages, total citations, average citations per publication, and citations per year (CPY) were calculated in Microsoft Excel 2019. Price’s law was used to identify core authors and high-frequency keywords, and Bradford’s Law was applied to identify core journals. Network indicators, including total link strength and betweenness centrality, were used to evaluate the influence and bridging roles of authors, countries, and institutions in collaborative networks. Bibliometric mapping and visualization were performed using VOSviewer 1.6.20, CiteSpace 6.1.R1, Gephi 0.9.2, OriginPro 2024, and the Bibliometrix and ggplot2 packages in R. Because this study was based on bibliographic records rather than individual patient-level data, no formal inferential statistical testing was performed.

Results

Quantitative analysis of publication

Over the past 15 years [2010–2025], 845 relevant publications were identified, including 789 research articles and 56 review articles. Figure 2 displays the trend of annual publications in this field. From 2010 to 2015, the annual publication volume increased steadily, rising from about 20 articles to 53 articles, accounting for 2.4% and 6.3% of the total, respectively. Starting in 2016, the number of publications sharply increased. Approximately 70 papers were published in 2016, and the number peaked at around 85 in 2017. By 2019, the annual output had reached about 81 papers. This peak phase accounted for approximately 9–10% of the total publications. Since 2020, the annual publication output has shown an overall downward trend, although it has remained around 50 papers per year. About 60 papers were published in 2022, decreasing to around 48 in 2023, representing approximately 6% of the total literature. In 2024, output slightly rebounded to 52 papers, but due to a limited observation period, only 24 papers were recorded for 2025 (less than 3%).

Figure 2 Annual output of research of lymphatic metastasis of ovarian cancer.

Author analysis

Analyzing publication frequency per author helps identify influential scholars and primary contributors in a specific field. Bibliometric analyses often use Price’s principle to identify central authors in a research area (18). Price’s principle suggests that core contributors must produce at least 0.749 times the square root of the highest output among all authors. This threshold helps distinguish core authors from peripheral ones based on publication volume. According to this principle, the threshold for core authorship in ovarian cancer LNM research is approximately 2.59 publications. In this study, authors with at least three publications were considered core authors. Through VOSviewer analysis, 227 core authors were identified in this field. Table S1 provides detailed data on the top 10 authors by publication volume. Among these, Giovanni Scambia ranks first with 12 publications, and his academic work has been widely cited, with a total of 300 citations, averaging 25 citations per article, demonstrating a strong academic influence.

A total of 4,448 authors contributed to research on ovarian cancer LNM. A collaboration network of authors with five or more publications was constructed (Figure 3A). Giovanni Scambia, Anna Fagotti, Fanling Meng, Andreas du Bois, and Philipp Harter appear as the largest nodes in this network, as they have the highest publication volumes. Strong collaborations are observed among many authors. For example, Anna Fagotti works closely with Giovanni Scambia, E. Vizza, E. Nero, and Fabio Giorgi. Similarly, Nicolo Bizzarri collaborates frequently with Emilia Nero and others. Co-citation analysis identified 13,298 scholars, with 23 having been cited more than 50 times. Philipp Harter ranks first in terms of citation frequency with 155 citations, followed closely by Ahmed Jemal and Maria Kleppe with 143 and 123 citations, respectively. A co-citation relationship network between authors was created using a threshold of 30 citations (Figure 3B), revealing tight academic connections among researchers such as Pierluigi Benedetti Panici, James K. Chan, Philippe Morice, and Philipp Harter. Networks also analysis revealed that among the core authors, Giovanni Scambia had the greatest Total link strength, indicating the most extensive collaborative activity. Meanwhile, Philipp Harter exhibited the highest Betweenness Centrality, suggesting a pivotal role as a bridge connecting distinct research subgroups within the collaborative network.

Figure 3 The visualization of authors (A) and co-cited authors (B).

Countries and affiliations analysis

By examining the source countries of publications, we can understand the international landscape of research on ovarian cancer LNM and identify the leading countries in this field. Research data shows that 50 countries and regions have contributed to the literature on this topic. Figure 4A displays the distribution of publications by country of the corresponding author, with China, the United States, Italy, and several European countries highlighted. Table S2 lists the top 10 countries by publication count. China ranks first with 501 papers (61.0% of the total), followed by the United States with 70 papers (8.5%) and Italy with 45 papers (5.5%). In terms of citation frequency, the Netherlands leads with 28.0 citations per article, followed by the United States (26.9 citations) and Germany (23.7 citations). Despite leading in publication volume, China’s average citation count per paper is lower (19.8 citations), compared to the Netherlands, United States, and Germany.

Figure 4 Country scientific production: darker shades indicate a higher number of publications (A), cooperation relationships between countries (B), top 20 countries’ publication partnerships (C), and growth trends of the top 5 institutions (D). MCP, multi-country publications; SCP, single-country publications.

Figure 4B illustrates the collaboration network among 27 high-performance countries, highlighting the strong ties between China and the United States, as well as close collaboration among several European countries, including the United Kingdom, Germany, and Italy. Analysis of the collaboration network metrics provides further nuance. While China leads in both publication volume and total link strength, indicating the most extensive collaborative ties, countries such as the United States , and the Italy exhibited notably high betweenness centrality. This suggests that beyond being prolific collaborators, these countries serve as critical hubs bridging research activities across different geographic regions, such as between Asian, North American, and European research clusters. Figure 4C further illustrates international collaboration patterns in ovarian cancer LNM research. Publications with all authors from the same country are categorized as single-country publications (SCP), while those co-authored by researchers from multiple countries are classified as multi-country publications (MCP). The results indicate high levels of international collaboration, consistent with the findings in Figure 4B. However, a country’s total publication count affects its absolute number of MCPs. To better assess collaboration levels, the MCP% metric (MCP/total publications ×100) was introduced, with values given in the last column of Table S2. By this measure, France has the highest MCP% (56.3%), followed by the United States (37.1%) and the Netherlands (28.6%).

Additionally, an investigation of participating institutions revealed key research groups in this field. The search included 1,042 institutions (Table S3), with the top 10 institutions located in China. Table S3 lists these institutions, indicating that substantial government funding supports extensive research efforts in Chinese academic centers. Figure 4D shows the top 5 universities by publication count over the past decade. Fudan University, which began research on ovarian cancer LNM in 2012, has consistently led in output. Network analysis confirmed Fudan University’s central role, as it also demonstrated the highest total link strength among all institutions, reflecting its active and extensive collaborative partnerships.

Journal analysis

As the primary platform for disseminating scientific research, academic journals play a crucial role in measuring a field’s influence, both in terms of publication volume and citation frequency (19). The literature retrieved in this study was published in 229 different journals. According to Bradford’s Law, Figure 5A presents the distribution of 12 core journals, which account for 20% of all publications. Table S4 lists the top 10 journals by publication volume. The International Journal of Gynecological Cancer (IJGC) ranks first in terms of publications in this field. This journal is a Journal Citation Reports (JCR) Q1 [2024] publication focusing on gynecological tumors, including screening, prevention, treatment, and clinical studies. In terms of citation frequency, gynecological oncology journals have performed exceptionally well. Gynecological Oncology, the official journal of the Society of Gynecological Oncology (SGO), holds a prominent position in this field. Four of the top ten journals (40%) are open access.

Figure 5 Source clustering through Bradford’s Law (A) and the dual-map overlay of journals (B).

Figure 5B shows a dual graph overlay analysis of cited journals. The left side indicates the discipline classification of the cited journal, while the right side represents the discipline category of the citing journal. Yellow and green highlight three main citation paths, demonstrating the academic connections between journals. The yellow path indicates citations from molecular, biological, and genetic research journals, while the green paths reflect citations from medical/clinical journals.

Literature co-citation analysis

The number of citations a publication receives is often used as an indicator of its academic impact over time (20). By analyzing citation counts, we can identify the most influential works in a field and trace the development trajectory of that research area. Table S5 presents the top 5 most-cited publications in the ovarian cancer LNM field over the past 15 years. To enable a fairer comparison that accounts for differences in publication time, we have calculated the CPY for each entry. All five of these highly cited papers were published in prestigious medical journals. Four of them are basic experimental studies, while one is a translational research article. Beyond individual highly-cited works, the 56 review articles identified in our study provide a critical lens to assess the field’s knowledge synthesis. A dedicated examination of these reviews reveals a substantial body of high-quality syntheses, including seminal reviews that consolidated prognostic and mechanistic factors, as well as more recent reviews summarizing advances in surgical techniques and the tumor immune microenvironment.

When two references are cited together in the same paper, this is called co-citation. By calculating the co-citation frequency, the degree of correlation between two articles can be evaluated (21), deepening our understanding of the knowledge system in a given research area (22). Using VOSviewer, we performed a co-citation analysis. Figure 6 illustrates clusters of 48 documents with citation counts greater than 20. The analysis revealed that the red cluster primarily focuses on surgical interventions and clinical decision-making for ovarian cancer LNM, while the green cluster emphasizes treatment effectiveness and prognostic evaluation. The blue cluster predominantly explores epidemiological characteristics and disease burden. Overall, research on ovarian cancer LNM can be categorized into two major domains: clinical applications and basic science exploration.

Figure 6 Document co-citation network.

Analysis of keyword clusters and timelines

Keywords represent the central focus of a research paper. In a given field, high-frequency keywords reflect the research hotspots during specific periods (23), Wei et al. (24) found that Price’s law applies not only to identifying core authors but also to defining high-frequency keywords in a field. In studying lymphatic metastasis in ovarian cancer, they set a threshold of five occurrences for high-frequency keywords. Accordingly, the present study considers any term appearing five or more times as a high-frequency keyword. The co-occurrence of terms refers to the simultaneous occurrence of two words within the same text. VOSviewer uses probabilistic normalization techniques to generate intuitive and easy-to-understand keyword cluster maps (25). This study used VOSviewer to construct and analyze a keyword co-occurrence network. The proximity between dots reflects the strength of their correlation. Keywords with stronger connections tend to cluster together. Figure 7A shows that high-frequency keywords are grouped into five relatively independent thematic clusters. Specifically, the red cluster involves research themes like surgical staging, lymph node dissection (lymphadenectomy), and perioperative treatment; the green cluster focuses on in-depth research into gene regulatory pathways and molecular prognostic factors driving lymphatic metastasis; the blue cluster explores emerging areas such as immune microenvironment studies, tumor-infiltrating lymphocytes, and in vitro functional validation; the yellow cluster reflects the trend of utilizing large cohort data and statistical models to quantify metastasis risk and prognostic factors; the purple cluster suggests increasing attention to molecular targeting of lymphangiogenesis and retrospective clinical analyses. Additionally, the keyword density visualization in Figure 7B shows the distribution of each keyword in the literature, where warmer (redder) colors indicate higher frequency of occurrence.

Figure 7 Keyword clustering map: nodes represent keywords, sized by occurrence frequency and colored by thematic cluster; links denote co-occurrence relationships (A), keyword density map (B), top 15 keywords with the strongest citation bursts (C), and three-field plot of countries, institutions, and keywords (D).

We also conducted a keyword burst detection using CiteSpace. Burst keywords are those whose frequency increases sharply within a short time, reflecting shifts in research hotspots (26). In this study, we analyzed keyword bursts by both their period and intensity using CiteSpace’s features (27,28), The period of a burst reflects the development trend of the research topic (29). Figure 7C presents the top 15 keywords with the strongest burst intensity. Over time, several keywords related to ovarian cancer and metastasis showed notable bursts. For instance, “lymphadenectomy” had a sustained burst from 2021 to 2025 (lasting 4 years), “lymph node metastasis” showed a burst from 2010 to 2013 (lasting 3 years), “epithelial ovarian” had a burst from 2021 to 2025 (lasting 4 years), and “prognostic factors” had a burst from 2014 to 2019 (lasting 4 years). Additionally, terms like “sentinel lymph node” [2022–2025], “lymphatic metastasis” [2023–2025], and “risk” [2022–2025] have shown burst patterns recently.

Figure 7D further illustrates the distribution and proportions of core themes across countries and institutions, highlighting the connections between countries, institutions, and keywords in ovarian cancer LNM research. From a research institution perspective, Chinese universities such as Shandong University, Fudan University, and Harbin Medical University stand out. These institutions focus on key themes like “carcinoma”, “expression”, “metastasis”, and “survival”. Notably, Shandong University has made significant contributions to cancer and expression-related research. The research on “cancer” and “expression” has the highest concentration. Additionally, “lymph node dissection”, “invasion”, and “cell” are mentioned frequently, with related research conducted in countries like China, Italy, the United States, and Japan.

Keyword timeline analysis

Keyword timeline analysis helps us track the evolution of research themes in a particular field (30). CiteSpace uses a set theory-based data normalization method to measure the similarity of data units in its timeline view. This approach helps illustrate the evolution and transition of research hotspots over time (31). Accordingly, we used CiteSpace to conduct keyword temporal analysis. Figure 8A shows that both fundamental and clinical researches on ovarian cancer LNM have followed a common development trajectory, complementing and promoting one another. Research has shifted from focusing solely on clinical manifestations and pathological characteristics in the early stages to emphasizing precise diagnostic and treatment strategies and underlying molecular mechanisms. Timeline analysis indicates that around 2016, terms like “immune microenvironment” and “T cells” began to appear frequently.

Figure 8 Timeline view of keyword co-occurrence clusters: size = frequency; color = cluster; position = cluster; first-appearance year; curves show co-occurrence; vertical axis is time (A) and keyword temporal trend plot: node size shows term frequency; gray lines depict active periods (B).

Figure 8B shows the yearly evolution of the research focus, highlighting the top three keywords that occurred more than 10 times per year. Notably, “lymph node metastasis” was first listed as keywords in 2017 and 2019, respectively, showing significant growth around 2021. Over the past three years, emerging terms like “nomogram”, “HGSOC” (High-Grade Serous Ovarian Cancer), “SEER” (Surveillance, Epidemiology, and End Results Cancer Database), and “biomarkers” have gradually attracted attention from the academic community.

Themes and thematic evolution

Using clustering techniques on the keyword network, we created a thematic map of the field’s core research directions, including marginal themes, emerging trends, hot topics, and fundamental research areas. We used the Walktrap algorithm to perform the clustering analysis on the keyword network. Figure 9A shows that “early ovarian cancer”, “sentinel node”, and “laparoscopy” were hot topics, while “lymphadenectomy” and “prognosis” were basic, cross-cutting themes. “Circular RNA”, “cardiophrenic lymph node”, and “chemoresistance” emerged as isolated topics. Figure 9B illustrates the major thematic areas and their evolution between 2010–2018 and 2018–2025, highlighting thematic transitions. Specifically, research themes evolved from “CA125”, “cancer stem cells”, and “chemotherapy” toward a focus on “ovarian cancer”; simultaneously, themes like “advanced ovarian cancer” and “cancer” shifted towards “lymphadenectomy”.

Figure 9 Thematic map and thematic evolution of research on lymph node metastasis in ovarian cancer. (A) Thematic map and (B) thematic evolution.

Discussion

This comprehensive bibliometric analysis maps the intellectual landscape and dynamic evolution of research on LNM in ovarian cancer over the past 15 years. Beyond quantifying output, our study reveals distinct patterns in collaboration, knowledge dissemination, and thematic shifts, offering data-driven insights into the field’s maturation and future trajectories. Our analysis reveals a non-linear growth in annual publications, characterized by a sharp rise peaking around 2017–2019, followed by a recent plateau and modest decline. This surge may be attributed to several converging factors: increased translational research bridging molecular biology and clinical oncology, the publication of pivotal trials and guidelines emphasizing surgical staging, and a growing research focus on the prognostic implications of nodal disease. This recent plateau could reflect a maturation of certain clinical questions, a shift in research priority towards other aspects of ovarian cancer biology. Despite this moderation in volume, the sustained publication activity underscores the enduring academic and clinical relevance of understanding and managing lymphatic spread in ovarian cancer.

The global research effort is markedly asymmetric, with China contributing over 60% of the total output. While China leads in both productivity and total collaborative links, countries like the United States and Italy exhibit high betweenness centrality. This indicates that these nations act as critical knowledge bridges, connecting disparate research clusters across Asia, North America, and Europe. This collaborative architecture suggests that while resource concentration drives high output, cross-pollination of ideas and interdisciplinary innovation often flow through these well-connected hubs.

Co-citation and journal analysis elucidate the field’s dual pillars and dissemination channels. The co-citation network clusters firmly into clinical-surgical and basic science domains, confirming that the field’s knowledge base is equally rooted in operative oncology and molecular biology. This is mirrored in journal citation paths, where a strong flow exists between core gynecologic oncology journals and those focused on molecular biology and immunology. The dominance of specialized, high-impact journals like the International Journal of Gynecological Cancer and Gynecologic Oncology for publication confirms the field’s clinical expertise, while the significant presence of open-access journals (40% of top outlets) reflects a commitment to broader knowledge dissemination.

The most dynamic insights come from the keyword and thematic evolution analysis, which quantitatively captures the field’s paradigm shift. The clear progression from clusters centered on “lymphadenectomy” and “staging” to those dominated by “EMT”, “immune microenvironment”, and “biomarker” signals a fundamental transition from a primarily anatomy-driven, surgical discipline to a molecularly-defined, precision oncology field.

The sustained burst strength of “lymphadenectomy” [2021–2025] objectively mirrors the intense and unresolved clinical debate regarding its therapeutic value. This surgical focus is rooted in the clinical significance of LNM, which is found in a subset of patients (approximately 14%) but can involve critical sites like the pelvic and iliac lymph nodes in over 70% of low-grade serous ovarian cancer cases (32,33). This bibliometric pattern directly corresponds to pivotal research findings: while lymph node involvement is a recognized adverse prognostic factor (34,35), its clinical management is contested. Major trials such as LION have shown that for advanced-stage patients without gross nodal disease, systematic lymphadenectomy does not improve survival (36-38), prompting a re-evaluation of its role. Consequently, the current research focus, as seen in our thematic maps, has evolved from advocating for universal dissection towards optimizing patient selection and defining nuanced indications (39,40), fueled by advances in diagnostic imaging (41,42).

Concurrently, the emergence of “sentinel lymph node” (SLN) as a burst keyword [2022–2025] reflects the research community’s active pursuit of less morbid surgical strategies. This trend is supported by clinical studies confirming that SLN biopsy improves detection rates and diagnostic accuracy while reducing complications associated with systematic dissection (43,44), representing a shift towards precision staging. Preliminary studies report detection rates of 80–90% in early-stage disease (45,46), though standardization is needed. Furthermore, the growing prominence of “nomogram”, “risk”, and biomarkers in our keyword timelines highlights a parallel surge in research aimed at molecular prognostication and personalized risk assessment, moving beyond purely anatomical staging.

The shift towards basic science is substantiated by research exploring specific pathways that provide mechanistic explanations for lymphatic spread. Investigations into epithelial-mesenchymal transition (EMT) (47) and long non-coding RNAs (e.g., UCA1) (48) have revealed how tumor cells acquire invasive phenotypes, directly informing the “molecular regulatory pathways” cluster identified in our analysis. Similarly, the focus on the “tumor immune microenvironment” seeks to explain the biological context of metastasis. Techniques like immunohistochemical ultrastaging are being applied to evaluate these mechanisms in nodal tissues (49). These molecular insights, while promising, underscore the challenge of translating mechanistic understanding into validated clinical tools (50), a frontier clearly marked by our bibliometric maps.

Limitations

This study has several limitations. First, although both WoSCC and Scopus were initially considered, the final analysis included only WoSCC data due to its larger volume, which may introduce selection bias. Secondly, non-English literature databases were not included, potentially missing high-quality research published in other languages. Third, recent studies in less influential journals may have been overlooked due to low citation counts. These limitations imply that our conclusions may not fully reflect the current state of research in this field.

Conclusions

Although our understanding of ovarian cancer LNM has deepened and significant breakthroughs have been made in diagnosis and treatment, patient outcomes in metastatic cases remain poor. There is an urgent need to intensify research efforts to improve these outcomes. Addressing this challenge will require strengthened interdisciplinary collaboration, international cooperative research, and the adoption of innovative methodologies. Future research should focus on several key areas: (I) in-depth exploration of the molecular mechanisms underlying LNM; (II) development of high-precision imaging techniques and specific biomarkers to better identify metastatic lesions; (III) clinical studies to refine and standardize the extent of lymph node dissection; and (IV) the innovation of treatment strategies to suppress lymphatic metastasis and prevent recurrence. Addressing these core issues will be critical for the future of ovarian cancer LNM research.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the BIBLIO reporting checklist. Available at https://gpm.amegroups.com/article/view/10.21037/gpm-25-47/rc

Peer Review File: Available at https://gpm.amegroups.com/article/view/10.21037/gpm-25-47/prf

Funding: This work was supported by the Corps Key Areas of Science and Technology Research Programme (grant Nos. KC2190, KY000209); the “Tianshan Talents Project” Medical Special Leading Talents (grant No. CZ001214); and the 2024 Science and Technology Plan of the 8th Division (grant No. 2024ZDYL09).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://gpm.amegroups.com/article/view/10.21037/gpm-25-47/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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