Flow chart for literature search and screening. - Wenxi Li (542567) (2025)
Existing research on symptom management in patients with breast cancer has predominantly focused on symptom cluster construction. However, the mechanisms underlying symptom interactions remain unclear. This disparity has hindered the development of efficient and precise strategies for symptom management. Symptom network analysis offers a novel approach by visualizing the complex interrelationships and interaction strengths among symptoms, thereby enabling the identification of central and bridge symptoms. This scoping review aimed to map the symptom network structure in patients with breast cancer and identify the core and bridge symptoms, thereby providing a theoretical foundation for developing personalized and precise symptom management strategies in clinical practice. Searches of PubMed, Web of Science, Cochrane Library, Embase, Elton B. Stephens Company (EBSCO), China National Knowledge Infrastructure (CNKI), Wanfang Data, and SinoMed were conducted to retrieve relevant literature on breast cancer symptom network analysis published from the time of database initiation to January 31, 2025, and assess the data. Thirteen papers were included, of which five were in Chinese and eight were in English. Symptom-related assessment tools included 13 types of single-symptom assessment scales and multi-symptom assessment scales, of which five studies used two or more scales; a total of 10 core and bridge symptoms were extracted, of which fatigue was the crucial core and bridge symptom. The symptom networks of breast cancer patients differ and change dynamically under varied treatment modalities. Although research on the symptom networks of patients with breast cancer has progressed, pitfalls such as unbalanced development and non-uniform research methods remain. Large-scale longitudinal studies are required to frame dynamic symptom networks, develop specific measurement tools, and combine latent variable analyses to construct heterogeneous symptom networks that will facilitate precise symptom management.