Research Trends on Cancer-Related Cognitive Impairment in Patients with Non-Central Nervous System Cancer: Text Network Analysis and Topic Modeling

Abstract

Purpose: This study aimed to understand the knowledge structure and trends in research on cancer-related cognitive impairment (CRCI) in patients with non-central nervous system (non-CNS) cancer through text network analysis and topic modeling. Methods: From 2011 to 2021, studies on CRCI in patients with non-CNS cancer registered in databases including Ovid-MEDLINE, EMBASE, Cochrane, CINAHL, CENTRAL, and PsycInfo, were extracted and cleaned into words using Python’s natural language toolkit package. Text network analysis was performed using the NetworkX library, and topic modeling analysis based on the latent Dirichlet allocation algorithm was carried out using the Gensim library. Results: In total, 24,030 keywords were extracted from the abstracts of 490 selected papers, of which “chemotherapy,” “breast cancer,” and “quality of life” showed high frequency and centrality. As a result of the topic modeling analysis, four subject groups were derived, including cognitive impairment due to chemotherapy, breast cancer and cognitive impairment, factors related to cognitive impairment, and symptom experience. Conclusion: These findings will help cancer researchers to understand the trends and insights of research on CRCI in patients with non-CNS cancer and suggest important areas and directions for future studies.