Background: In order to reduce the cancer burden on the population, the National Cancer Institute (NCI) established the Surveillance, Epidemiology, and End Results Program Database (SEER) for tumor patients in the country in 1973. The huge amount of information in the SEER database provides a powerful data support for in-depth study of tumors. Therefore, there are a large number of medical studies that use the data provided by SEER for analysis and research.
Objectives: The research aims to comprehensively analyze the current status of studies based on SEER databases, and to understand the research hotspots and development trends of these studies.
Methods:Publications and their literature information were retrieved from the Web of Science Core Collection database and the time span was defined as “all years”. We used Microsoft Excel 2013 to detect the trend of annual numbers of publications, and used VOSviewer 1.6.9 software as the bibliometric method to analyze the research areas, countries/regions, institutions, authors, journals, research hotspots and frontiers, and development trends.
Results: A total of 7,249 related studies based on the SEER database were included in the bibliometric analysis. In 1980, the first related research based on the SEER database was published, and the number of publications with an increasing trend, even reaching 1048 studies in 2019. More than half of the studies was produced after 2015. The studies was published in 1,084 journals, and a total of 19,740 authors from 89 countries or regions participated in the relevant research. The most prolific country and institution were the USA and NCI, respectively. Karakiewicz PI was the most productive author, while Cancer was the most prolific journal. Relevant literature mainly focused on the field of oncology.
Conclusions: Interest in the SEER database is increasing year by year, and big data-oriented cohort research has become a research hotspot. Researchers should attach importance to the role of controlled study in data analysis. Through the integration and transformation of biomedical big data, it can help generate evidence-based scientific evidence.
Patient or healthcare consumer involvement: Not applicable.