Abstract: Objective: To develop a relationship network between symptoms and Quality of Life (QoL) in Nasopharyngeal Carcinoma (NPC) survivors, and to explore the pathways and key nodes through which symptoms affect the QoL, in order to provide references for formulating individualized symptom management strategies and improving patients' QoL. Methods: A total of 398 survivors with NPC who returned to a tertiary grade A cancer hospital in Guangzhou for re-examination after completing comprehensive radiotherapy and chemotherapy were recruited from May to November 2024 by convenience sampling. The General Information Questionnaire, the M. D. Anderson Symptom Inventory-Head and Neck Module and the Functional Assessment of Cancer Therapy-Head & Neck were used to investigate NPC survivors. The symptom clusters were extracted through exploratory factor analysis, and the network analysis was conducted using R software to construct a symptom-QoL network, identify associations between nodes, and evaluate the centrality indices of each node. Results: Three symptom clusters were extracted in NPC survivors: oral sickness symptom cluster, psycho-neurological symptom cluster and head and neck function-gastrointestinal symptom cluster. The total score of QoL of NPC survivors was 99.87±0.97, and the average score of functional status dimension was the lowest, which was 2.17±0.05, and it served as the core node within the QoL network (rs=1.10). In the combined network, the intensity of the psycho-neurological symptom cluster was the largest (rs=1.20), which also functioned as the most important bridge symptom connecting the symptom network and the QoL network (rbs=1.50). Conclusion: Nasopharyngeal carcinoma survivors experience different clusters of symptom. Moreover, these symptom clusters are closely associated with QoL. The psycho-neurological symptom cluster, serves as the key bridge node in the pathway through which symptoms impact QoL, which forms the core intervention target.

Key words: Nasopharyngeal Carcinoma; survivor; symptom cluster; Quality of Life; network analysis