Bibliometrics of gastric cancer prediction models

Abstract

Background: Bibliometric analysis highlights the key topics and publications in gastric cancer prediction. This paper analyzes the manuscripts in the field of gastric cancer (GC) prediction, guiding clinical work and prevention of GC. Methods: Using a search strategy, we retrieved research articles related to GC prognosis from the WOS core database: 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 1598 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 1056 articles, excluding 542 articles, and extracted the required data from the WOS database for analysis. The extracted database included: title, publication year, author, country, institution, citation count, journal, keyword, and reference. We used R (4.3.0) to load the R package (bibliometrix) for bibliometric analysis. Results: The 1056 articles came from 273 sources (journals, books, etc.), and 3661 authors conducted relevant research on GC prognosis models. Oncology Frontiers published the most articles (N=72), and Gastric Cancer Journal had the most citations (N=1130). The publication time span ranged from 1991 to 2023, with an average annual growth rate of 13.31%. The number of publications increased from 2017, with a sharp increase from 2020 to 2023. The five countries with the most publications were China (n = 826), Japan (n = 62), Korea (n = 47), USA (n = 42), Italy (n = 19) and 1998 (n = 10). China had the most citations (N=9595), and USA had the highest average citation per article (44.9 times). The most common topic was GC survival (n=236), followed by expression (n=209). Conclusion: Multiple GC prediction models in this study describe the science of predicting GC incidence and prognosis. This work provides the most influential references related to GC prediction and serves as a guide for citable papers.