Bibliometrics of gastric cancer prediction models

Introduction

Gastric cancer (GC) is a very common malignant tumor in every country/region (1). In 2020, there were about 1.1 million new cases and 770,000 deaths from GC worldwide. The age-standardized mortality rate of GC decreased from 25.8 per 100,000 people in 2000 to 18.5 per 100,000 people in 2017 (2). The incidence and mortality of GC vary greatly among different countries, regions, and human development index (HDI) levels. The incidence and mortality of men are usually twice as high as those of women. East Asia has the highest incidence and mortality, with Japan, Mongolia, and Korea having the highest male incidence in the world. Africa has the lowest incidence and mortality. It is estimated that by 2040, the annual number of GC cases will increase to 1.8 million and the number of deaths will increase to 1.3 million (3).

Improving the prognosis of GC patients has a good effect on reducing the mortality of GC patients (4). The prognosis of GC is influenced by many factors, such as tumor stage, histological type, molecular markers, treatment methods, patient age, gender, underlying diseases, etc. Early GC has a good prognosis, with a 5-year survival rate of up to 95%. However, the prognosis of advanced GC is poor, with a 5-year survival rate of only 18–50%. Therefore, improving the early diagnosis and treatment of GC is the key to improving the prognosis (5). In recent years, with the prevalence of big data, various prediction models for diseases have emerged, which can predict the prognosis of GC patients and intervene in time, such as choosing appropriate treatment plans, evaluating treatment effects, making follow-up plans, etc., bringing greater life benefits to patients. In recent years, various prediction models have used different data sources and different calculation methods to establish many GC prognosis prediction models (6).

Bibliometrics is a comprehensive analysis method based on literature itself. By analyzing the literature relationships comprehensively, it can understand the development status of a specific field, and discover the research hotspots and development trends hidden behind the literature through in-depth analysis and visualization of data (7). This bibliometric study aims to provide a comprehensive overview of the current knowledge and understanding of prediction models. This review addresses the following research questions: the purpose is to describe how the literature characteristics of clinical papers that have been published so far with GC prognosis prediction as the primary or secondary outcome have changed over time, and to judge the direction and development trend of GC prognosis prediction in the future based on the literature analysis results (8).

This article addresses the following research question: what are the main research directions of GC prediction, and what are the trends of the existing prediction models? This article shows that the research on GC prediction models has increased gradually in recent years, and some studies indicate that more accurate prediction models can improve the survival rate of patients (9,10). However, the literature also emphasizes the necessity of further research, to fully understand the various risk factors considered by different GC prediction models and to develop corresponding preventive and therapeutic measures. This study will fill the gap in the existing literature by synthesizing the current findings and trends in this field, and provide a comprehensive overview of the current knowledge and understanding of GC prediction models for researchers, clinicians, and policy makers (11).

Methods

Data source and literature search strategy

We used a search strategy to retrieve research articles related to GC prognosis from the Web of Science (WOS) core database (12): topic search (TS) = ((gastric cancer OR stomach cancer) AND (survival rate OR survival analysis OR prognosis) AND (predict model)). We set the language to English, the document type to article and review, and completed the search on July 1, 2023. We obtained 1,598 relevant articles, and two researchers screened the search results again, excluding irrelevant, misclassified, and retracted articles. Any controversial articles were reviewed by a third researcher to make the final decision on the required literature. We finally selected 1,056 articles, excluding 542 articles, and extracted the required data from the WOS database for analysis. The extracted database included: title, publication year, author, country/region, institution, citation count, journal, keyword, and reference (13).

Bibliometric analysis software

We used R version 4.0.17 and R package (Bibliometrix) as the software tools for performing bibliometric analysis.

Results

The 1,056 articles came from 273 sources (journals, books, etc.), and 3,661 authors conducted relevant research on GC prognosis models. The publication time span ranged from 1991 to 2023, with an average annual growth rate of 13.31%. There were only 3 independent researchers, and the proportion of international collaboration was 10.51%. The average number of co-authors per article was 7.87, and there were 1,681 keywords in the articles. The original references were 26,395.

“Sources”: some analysis of the literature sources

As shown in Figure 1, the literature selected in this study came from 273 journals. The journals that published the most articles in the field of GC prognosis prediction were Frontiers in Oncology (N=72), Frontiers in Genetics (N=43), Journal of Cancer (N=26), Annals of Surgical Oncology (N=23) and BMC Cancer (N=22) (Figure 1).

Figure 1 Distribution of selected literature across 273 journals.

The references of the selected articles came from 3,015 journals, among which Gastric Cancer had the most citations (N=1,130), followed by Journal of Clinical Oncology (N=1,019), Annals of Surgical Oncology (N=885), CA-Cancer J Clin (N=762) and Cancer Research (N=615) (Figure 2).

Figure 2 Distribution of references across 3,015 journals.

According to Bradford’s law, the journals that published articles on GC prognosis prediction were divided into three parts, of which the core sources included 15 journals, with the highest frequency being Frontiers in Oncology (N=72), followed by Frontiers in Genetics (N=43) and Journal of Cancer (N=26) (Figure 3).

Figure 3 Core journals identified by bradford’s law in gastric cancer prognosis prediction.

According to the H-index, the selected journals were ranked, and the journal with the highest H-index was Annals of Surgical Oncology (H=14) (Figure 4).

Figure 4 Ranking of journals by H-index, with Annals of Surgical Oncology achieving the highest H-index (H=14).

According to the number of publications, the cumulative number of publications of the top 5 journals was plotted as a line chart, which can be divided into three stages according to the change of cumulative number of publications over time: the first stage was from 2000 to 2013, marking the beginning of the field of GC prognosis prediction, and only Annals of Surgical Oncology published articles on this field; the second stage was from 2014 to 2019, and the number of articles on GC prognosis prediction increased continuously, and all journals published articles on this field; the third stage was from 2020 to 2023, and the annual number of publications on GC prognosis prediction increased rapidly (Figure 5).

Figure 5 Cumulative publications of the top 5 journals in gastric cancer prognosis prediction over time.

“Authors”: author analysis

Author productivity and impact

A total of 3,661 authors contributed to the research in this field, and the activity and contribution of a scholar in a field can be reflected by the number of articles published. The top 3 authors in terms of publication volume in the field of GC prognosis prediction were Wang Y (N=89), Zhang Y (N=64), and Li Y (N=46) (Figure 6).

Figure 6 Top 3 authors by publication volume in gastric cancer prognosis prediction research.

From the perspective of citation count, Sano T had the highest cumulative citation count for published articles (N=58) (Figure 7).

Figure 7 Author with the highest cumulative citation count: Sano T (N=58).

The annual publication volume and average annual citation count of the top 10 authors in terms of publication volume in the field of GC prognosis prediction (Figure 8).

Figure 8 Annual publication volume and average citation count of top 10 authors in gastric cancer prognosis prediction. TC, total citations.

According to Lotka’s law, the productivity of authors was plotted as a graph, which showed that most authors had less than 10 articles in this field (Figure 9).

Figure 9 Author productivity distribution in gastric cancer prognosis prediction based on lotka’s law.

According to the H-index calculation, Li G and Yu J had the best H-index (H=14) for their impact on the local area (Figure 10).

Figure 10 Top authors by H-index: Li G and Yu J (H=14).

Affiliations

Regarding institutions, Sun Yat-sen University (N=214), Fujian Medical University (N=203), Fudan University (N=194), China Medical University (N=166), and Nanjing Medical University (N=153) had the most articles (Figure 11).

Figure 11 Top 5 institutions by publication volume in gastric cancer prognosis prediction research.

The output of the top 5 institutions by publication volume was plotted as a line chart over time, showing that after 2017, the publication volume of each institution increased rapidly (Figure 12).

Figure 12 Publication trends over time for the top 5 institutions in gastric cancer prognosis prediction.

Countries/regions

In comparison, China had the most publications in the field of GC prognosis prediction, whether in single- or multi-country/region publications (Figure 13).

Figure 13 Countries/regions comparison of single- and multi-country/region publications in gastric cancer prognosis prediction.

From the analysis of the number of published articles, China was the country with the most publications on GC prognosis prediction in the world (Figure 14).

Figure 14 Global distribution of publications on gastric cancer prognosis prediction. SCP, single country/region publication; MCP, multiple country/region publications.

The earliest articles in this field were published by Japanese authors in 1991, and after 2012, the number of articles published by China in this field increased rapidly, far exceeding other countries/regions (Figure 15).

Figure 15 Timeline of publications in gastric cancer prognosis prediction by country/region.

China is the country with the most citations (N=9,595), with an average of 11.6 citations per article, compared to the United States, which has an average of 44.9 citations per article (Figure 16).

Figure 16 Citation analysis: comparison between China and the United States in gastric cancer prognosis research.

“Documents”: article analysis

Documents

We analyzed the most cited literature in the world, and listed the 10 most cited articles in the table, with total citations ranging from 140 to 435. The most cited article was a study by Dr. Smith published in the Journal of Clinical Oncology in 2005, which was about the impact of total lymph node count after GC surgery on staging and survival (Figure 17).

Figure 17 Top 10 most cited articles in gastric cancer prognosis research.

We analyzed the most frequently cited literature, and the most cited article was a study by Dong-Seok Han published in the Journal of Clinical Oncology in 2012, which was about predicting long-term survival after GC D2 gastrectomy.

Cited references

We analyzed the references involved in the selected literature, and listed the 10 most cited articles in the table, with total citations ranging from 60 to 147. The most cited article was a study by Bray F published in 2018, which was about estimating the global incidence and mortality of 36 cancers in 185 countries/regions (Figure 18).

Figure 18 Top 10 most cited references in gastric cancer prognosis research.

Words

We analyzed the keywords of the selected literature, and listed the 10 most frequent keywords in the figure, with frequencies ranging from 69 to 236 times. The most frequent keyword was survival (N=236) (Figure 19).

Figure 19 Top 10 most frequent keywords in gastric cancer prognosis literature.

The frequency of keywords increased significantly after 2017 (Figure 20).

Figure 20 Co-occurrence analysis of keywords in gastric cancer prognosis research.

We used citation relationship (CR) to organize the literature and built a network graph to illustrate their relationships (Figure 21).

Figure 21 Citation relationship analysis and document coupling in gastric cancer prognosis research. (A) Clusters of documents based on citation relationships. (B) Coupling network of documents highlighting key publications. CR, citation relationship.

We visualized the keywords of the selected literature and built a keyword network graph (Figures 22,23).

Figure 22 Network analysis of keywords in gastric cancer prognosis prediction.

Figure 23 Cumulative node degree distribution of keywords in gastric cancer prognosis network.

We further analyzed the co-occurrence relationship between keywords and built a network graph to illustrate (Figure 24).

Figure 24 Co-occurrence analysis and thematic mapping of keywords in gastric cancer research. (A) Thematic map of keywords based on co-occurrence analysis. (B) Keyword co-occurrence network revealing major research clusters.

We visualized the keywords of the selected literature and built a keyword network graph (Figure 25).

Figure 25 Thematic and co-occurrence analysis of keywords in gastric cancer prognosis research. (A) Keyword network graph highlighting thematic clusters. (B) Hierarchical clustering dendrogram of keyword relationships.

Through our research, we identified the structure and key connections within the collaboration network (Figure 26). This analysis highlights the central nodes and collaborative patterns among researchers in the field.

Figure 26 Visualization analysis of collaboration network. (A) Citation network of key research papers. (B) Node degree distribution in the citation network.

We analyzed the collaboration relationship between authors and built a network graph to illustrate (Figure 27).

Figure 27 Author collaboration network analysis.

From the world map of collaboration, Asia and North America had the most frequent collaboration, with China and USA collaborating 40 times, Korea and USA collaborating 11 times, and USA and Japan collaborating 8 times (Figure 28).

Figure 28 Global collaboration map highlighting regional and country/region-level partnerships.

We built a correlation graph, with reference on the left, key words on the right, and authors in the middle (Figure 29).

Figure 29 Correlation graph linking references, keywords, and authors. CR, cited references; AU, authors; DE, descriptors.

From the frequency of article terms, survival and expression had the highest frequency, followed by carcinogenesis, cell, and chemotherapy (Figure 30).

Figure 30 Term frequency analysis: most common terms in gastric cancer research.

From the frequency of keywords, survival rate appeared 236 times, accounting for about 9%, expression and carcinogenesis appeared 209 and 142 times respectively, accounting for 8% and 5%, in addition, cell, chemotherapy, metastasis and other terms also had high frequency (Figure 31).

Figure 31 Keyword frequency analysis in gastric cancer prognosis research.

From the trend of article publication, the publication speed was relatively stable from 1991 to 2011, and the number of publications showed a significant upward trend from 2011 onwards, with a sharp increase in publication volume between 2019–2022 (Figure 32).

Figure 32 Publication trends in gastric cancer prognosis research (1991–2022).

The average citation rate per article stabilized steadily after 2003 (Figure 33).

Figure 33 Stabilization of average citation rate per article since 2003.

Basic information

In this study, we used the bibliometric method of R package (Bibliometrix) in R language to analyze the publications related to GC prediction. The results showed that the annual number of publications on GC prediction showed an overall upward trend, indicating that GC prediction was receiving more and more attention. The journals with the most publications were Frontiers in Oncology (n=72), Frontiers in Genetics (n=43), Journal of Cancer (n=26), Annals of Surgical Oncology (n=23), BMC Cancer (n=22), Oncotarget (n=21), Medicine (n=19), and the research on GC prediction was of interest to many scholars, whether it was oncology or genetics, and also received some attention in GC research. From the source of references, we can see that GC prediction was involved in both clinical and basic research of GC, and GC prediction played a very important role in the diagnosis and treatment of GC for the prognosis of GC patients. In GC research, we should also pay attention to predicting the future development trend and direction. Among many journals, from the H-index analysis, there were three journals with high output of GC prediction related articles: Annals of Surgical Oncology (H-index =14), Frontiers in Oncology (H-index =11), Oncotarget (H-index =10). From a comprehensive analysis of various aspects, we can see that the journals worth referring to in the field of GC prediction are: Annals of Surgical Oncology, Frontiers in Oncology, Oncotarget, and if you need to know more about GC prediction related articles, you can also refer to Journal of Cancer.

We can see from the publication year of the articles that from 2000 to 2013 was a slow growth stage, from 2014 to 2019 was a faster growth stage, which may be related to the development of imaging technology and the rapid development of detection level, the diagnosis rate of GC increased rapidly, and the content related to GC prediction also increased accordingly (14). From 2020 to 2023 was a rapid development stage of GC prediction, which was closely related to the rapid spread of the new coronavirus, more and more people paid attention to personal health issues, GC prediction also developed very rapidly, the prediction model, method and disease detection achieved great development, various prediction models, prediction formulas, personalized gene detection emerged, among which the journals with a sharp increase in publication volume were: Frontiers in Oncology, Frontiers in Genetics, Journal of Cancer, Annals of Surgical Oncology, BMC Cancer. In the field of GC prediction, the authors with the most publications were Wang Y (n=89) and Zhang Y (n=64), whose articles fractionalized were 11.53 and 8.76 respectively, and they were core researchers in the field of GC prediction. From the analysis of citation situation, the authors with the most citations were Sano T (n=58), Choi Y (n=57), Han DS (n=57), Lee HJ (n=57), Yang HK (n=57). From the H-index analysis of authors, Li G (H-index=14), Yu J (H-index=14), Liu H (H-index=13). From the analysis of publishing institutions, we can see from the figure that Sun Yat-sen Univ (n=214), Fujian Med Univ (n=203), Fudan Univ (n=194), China Med Univ (n=166), Nanjing Med Univ (n=153), these institutions belong to China, so there are more Chinese scholars in the field of GC prediction research. And from the analysis of all countries/regions where articles are published, China (n=826), Japan (n=62), Korea (n=47), USA (n=42), Italy (n=19), Japan was the first country to study GC prediction in 1991, and GC prediction research was mainly in Asian countries/regions such as China, Japan and Korea. This result may be closely related to the high incidence and mortality rate in Asia. From the analysis of country/region-related publications, China was the country with the most publications whether it was single country/region publications or multiple country/region publications, followed by USA’s MCP in second place. From the analysis of citation count, China’s total citation count was 9,595 times, but USA’s average citation count per article was 44.9 times. The most cited article was Smith DD’s article published in 2005, which was cited 435 times. The main content was a study on the impact of total lymph node count after GC surgery on staging and survival. The article with the most reference count was Dong-Seok Han’s article published in 2012 on predicting long-term survival after GC resection.

In addition, from the world map of collaboration, we can see that Asia and North America have the most frequent collaboration, with China and the United States collaborating 40 times, Korea and the United States collaborating 11 times, and the United States and Japan collaborating eight times. GC diagnosis rate and long-term survival rate are global issues that require international cooperation, especially between developed and developing countries/regions, to improve the popularization and application of GC prediction.

In this study, the main types of GC prediction research with the highest co-citation and co-referenced references are guidelines, reviews, and randomized controlled trials. Our analysis shows that guidelines, high-quality reviews, and randomized controlled trials may provide reliable evidence for more research on GC prediction.

Research hotspots and trends based on the top 10 frequency keywords, keyword cloud and cluster analysis, the research hotspots of GC mainly focus on the following aspects.

Long-term survival rate of GC

GC patients have a relatively poor prognosis, and one of the main reasons for the low survival rate of GC is the high metastasis rate of GC. About one-third of GC patients have metastases. Although non-curative treatment is usually the main treatment when GC metastasizes, it may still prolong life or relieve symptoms (15). Several treatment guidelines suggest that in some cases, patients with metastatic esophagogastric cancer should consider optimal supportive care. Since treatment is not always related to improving health-related quality of life, the best treatment option for a specific patient may not be obvious. In summary, personalized treatment and disease stage prediction for each patient are very helpful for the survival rate of GC patients (16).

Due to the complexity and heterogeneity of each GC patient, personalized treatment plans and related risks and benefits may be difficult. However, prediction models can improve this process and allow personalized decision making (17). Through bibliometrics, we found that various prediction models have been developed in recent years to help clinicians perform personalized role plans by predicting outcomes, such as survival and recurrence of cancer patients. For example, models predict the survival rate of GC patients based on various demographic and clinical variables. Prediction results can be utilized to compare various treatment methods by quantifying the additional survival benefits they provide (18). One study showed that almost all prediction models available for esophageal and GC are aimed at predicting survival after radical treatment. Van den Boorn et al.’s model predicted overall survival based on a dataset of 239 Korean esophageal squamous cell carcinoma patients (19). Van den Boorn et al.’s model included 64 metastatic adenocarcinoma patients to predict median overall survival in a line chart. Source aims to predict the overall survival of heterogeneous esophageal or GC patient groups with various treatment options (20).

Predicting GC incidence by gene expression

In recent years, genomic analysis can provide prognostic and predictive information on tumor biology. Gene testing can guide clinical care and improve treatment options for cancer patients (21). Currently, GC treatment is mainly systemic, but most patients rarely benefit from potentially toxic treatment. Some studies have shown that using fluorouracil-platinum doublet can only increase long-term survival rate by about 9%, but it brings more drug toxicity, and there is a lack of precision and personalized treatment in adjuvant therapy (22). Therefore, biomarkers are needed to predict the patient’s response to chemotherapy and improve treatment accuracy. Predict personalized chemotherapy regimens for different chemotherapy drugs by gene expression. Some studies have shown that although large-scale sequencing and molecular analysis will become the genomic development trend of GC, the mutation sequence of cancer genome has some randomness, and the functional analysis of gene-gene interaction and signal pathway dynamics has not been included, so there is some lack of completeness of the prognostic information source of gene testing (23,24). But some scholars have established a risk scoring model based on molecular subtypes to predict overall survival and response to chemotherapy and immune checkpoint blockade by genetic characteristics and gene testing results, and obtained better prediction results (25).

Chemotherapy for GC predicts the prognosis of GC

The current treatment for GC includes surgery combined with radiotherapy, chemotherapy or targeted therapy. Although the prognosis of patients is gradually improving, the long-term survival rate is still a problem that needs to be solved in our clinical work. Chemotherapy plays a very important role in the long-term survival rate of GC patients (26). Chemotherapy affects the tumor microenvironment (TME), and many centers have studied the relationship between tumor and TME in recent years. Fangyu Lin have found that tumor cells can induce immune escape by overexpressing programmed death-ligand 1 (PD-L1) protein and secreting interleukin inhibitors (27). In addition, tumor-associated fibroblasts surround tumor cells, inhibit immune cell infiltration and drug penetration, and cause treatment failure. Xiaoqi Mao have found that the enrichment level of TME cells can be used to evaluate the prognosis of GC (28). Therefore, analyzing the heterogeneity of TME, determining the quantitative indicators of TME, guiding the treatment strategy and prognosis evaluation of GC, may be the key to improve the efficacy and prognosis. Therefore, predicting the effect of chemotherapy drugs by the changes of TME is also a future development trend. In recent years, many TME-related molecules have been found, such as histone deacetylases (HDACs) (29). By analyzing the expression profile of HDACs and the corresponding TME characteristics, they provide theoretical basis for the clinical treatment strategy and prognosis evaluation of GC (30).

Surgical treatment and lymph node metastasis prediction for GC prognosis

Many retrospective studies have found that lymph node metastasis is an independent risk factor affecting the prognosis of early GC patients. The 2018 Chinese guidelines mentioned: for early GC patients with lymph node metastasis, gastrectomy combined with lymph node dissection is still the main treatment method, which can significantly reduce the local recurrence rate of tumor after surgery and improve the 5-year survival rate after surgery (31). For early GC patients without lymph node metastasis who meet the surgical indications, endoscopic mucosal resection (EMR) or endoscopic submucosal dissection (ESD) can be performed, which has no statistical difference in short- and long-term efficacy compared with traditional surgery; and it can significantly improve the postoperative quality of life of patients. In summary, if we can predict whether early GC has lymph node metastasis in advance, it will guide the rational choice of surgical methods in clinical work (32).

Strengths and limitations

Compared with traditional reviews, this article based on bibliometric tools provides better insights into the research hotspots and trends in the field of GC prediction, and the data analysis from various aspects is relatively more comprehensive and objective (33). However, due to the limitations of software and research methods, the selected articles are limited to the Web of Science Core Collection (WOS-CC) database, and articles included in other databases (such as PubMed and Chinese databases) have not been retrieved. Therefore, a total of 1,056 articles cannot fully represent all the available information in this field, which is the main limitation of this paper (34). Although we did not conduct a comprehensive analysis of every article related to this topic from all global databases, we still believe that this study can provide a description of the current situation and trends of GC prediction research, because WOS-CC contains a large number of high-quality core journal papers from around the world.

Conclusions

The trend of annual publications shows that GC prediction research is receiving more attention. By using bibliometric analysis, we identified the most active authors, countries/regions, institutions, and journals. Co-citation reference analysis revealed the top articles with important significance in this field. Keywords provide research hotspots and trends. In summary, this study not only provides the current situation and hotspots, but also provides the trends and frontiers of GC research, which may provide some new directions for future research in this field.

Acknowledgments

We thank the researcher, Dong Li, for his support and assistance with this paper.

Footnote

Peer Review File: Available at https://cco.amegroups.com/article/view/10.21037/cco-24-63/prf

Funding: This article was supported by a grant from the Clinical Surgery Category of the Shanxi Provincial Health and Health Commission under the topic or fund name: Research on the construction and validation of clinical prediction model for gastric cancer (No. 2020006).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://cco.amegroups.com/article/view/10.21037/cco-24-63/coif). All authors report that this article was supported by a grant from the Clinical Surgery Category of the Shanxi Provincial Health and Health Commission under the topic or fund name: Research on the construction and validation of clinical prediction model for gastric cancer (No. 2020006). The authors have no other 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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