Fear of cancer recurrence and perceived pain in patients with breast cancer: A network analysis approach.

Objective: This study aimed to identify core symptom nodes and examine directional relationships within the networks of fear of cancer recurrence (FCR) and pain catastrophizing (PC), and to investigate high-impact targets for intervention. Methods: From September to November 2024, a total of 346 eligible patients with breast cancer were enrolled from a multi-center trial named as Be Resilient to Breast Cancer (BRBC). The Fear of Cancer Recurrence Inventory and the Pain Catastrophizing Scale was used to collect data. A Gaussian network analysis was performed to identify the key components for FCR, PC and the connections between them. Bayesian networks were used to identify pathways of symptom activation at the symptom-level network architecture, and computer-simulated interventions were used to identify specific intervention targets. Results: In the analysis of separate networks, "Severity" emerged as the primary component of FCR, exhibiting the highest centrality metrics. For the PC, "Terrible" was identified as the central symptom, with notable centrality values. The dimension "Assurance" and the item "Awful" served as critical bridging elements, facilitating the interaction between FCR and PC when they co-occur. Bayesian network analysis identified 36 directed edges, with "Insight" in FCR and "Anxious" in PC acting as parent nodes, indicating their influential roles in the network structure. Computer-simulated interventions demonstrated that amplifying the "Terrible" node in PC maximized the total score and network connectivity. Conversely, attenuating the "Triggers" node in FCR minimized the total score. Conclusions: This study demonstrates that FCR and PC exhibit distinct network structures, which have their own specific core symptoms and corresponding core bridging nodes when the two coexist. This may serve as primary targets for personalized interventions for patients with breast cancer.