Navigating specific targets of psychoneurological symptom cluster in breast cancer: a computer-simulated network analysis.

OBJECTIVES

Sleep disturbance, cancer-related fatigue, emotional distress (anxiety and depression), and pain, collectively known as psychoneurological symptom cluster (PNSC), were common during and after anti-cancer treatment in breast cancer patients. This study aimed to explore the relationship among PNSC to identify potential intervention targets.

METHODS

In this cross-sectional study, 304 breast cancer patients who received treatment in the Breast Surgery Department from June 2025 to January 2026 were recruited. Self-report data were collected using the Cancer Fatigue Scale, the Pittsburgh Sleep Quality Index, Hospital Anxiety and Depression Scale, and Visual Analog Scale for Pain. Static symptom interrelationships were examined using Gaussian network model, and putative directional associations were explored using Bayesian network analysis. The dynamic correspondence between symptoms was explored through computer-simulated interventions, and simulation-derived candidate symptom targets were identified.

RESULTS

The network analysis identified physical fatigue, sleep latency, and depression as core symptoms, with expected influence (EI) values of 1.883, 0.940, and 0.794, respectively; furthermore, the study identified physical fatigue, pain, and daytime dysfunction as bridging symptoms, with bridge expected influence (bEI) values of 1.824, 1.558, and 1.331, respectively. The estimated Bayesian network also suggested that physical fatigue occupied as a key node within the network. The results of computer-simulated interventions showed that depression produced the largest reduction, with sum scores declining from 5.98 to 4.67; this was followed by the affective fatigue and physical fatigue. Meanwhile, poor sleep efficiency was associated with an increase in the total sum score, from 5.99 to 6.04.

CONCLUSIONS

Our findings suggest that physical fatigue and depression may be prioritized as intervention targets to break the vicious cycle of symptom interactions within the PNSC. The integration of computer-simulated interventions and traditional network analysis allowed for prioritizing the effects of individual symptoms at both static and dynamic levels, thereby improving the accuracy and effectiveness of targeted interventions. Further research is needed to verify whether targeting these symptoms can improve clinical outcomes.