Network-derived symptom architecture and simulated leverage points during radiotherapy for oral cancer

Objective: To characterize the symptom network among patients with oral cancer undergoing radiotherapy and explore potential symptom leverage points using network-based intervention simulation. Methods: Patients with oral cancer undergoing radiotherapy were recruited from a tertiary hospital in China between January and December 2024 and assessed using the M.D. Anderson Symptom Inventory-Head and Neck module. The primary symptom network was estimated from the original 0–10 symptom scores using a regularized Gaussian graphical model. Strength centrality, bridge strength and network stability were assessed. For the simulation analysis, symptom scores were dichotomized as absent or present, and a binary Ising model was estimated. Symptom-specific intervention simulations were implemented through threshold perturbations and repeated 1000 times, with additional sensitivity analyses performed to assess robustness. Results: Among the 508 participants, dry mouth, fatigue, and mouth or throat mucus were the most common symptoms. Four symptom clusters were obtained through factor analysis. Disturbed sleep showed the highest raw strength centrality (1.182), followed by mouth or throat soreness (1.084) and fatigue (1.077), whereas choking exhibited the highest bridge strength in the four-factor symptom network. No statistically significant differences in network structure or global strength were detected across subgroups defined by sex, anatomical site, or treatment modality, although these findings should be interpreted cautiously. In repeated simulations, pain showed the largest mean modeled reduction in the number of symptoms present under the alleviating intervention condition (mean Δ = −0.696), followed closely by mouth or throat soreness (−0.686). Vomiting showed the largest mean modeled increase under the aggravating intervention condition (mean Δ = +0.514) and ranked first in 75.7% of repetitions. Sensitivity analyses showed consistent effect directions and broadly similar overall ranking patterns. Conclusions: The findings identify several symptoms with relatively high network connectivity or greater modeled influence in intervention simulations implemented through symptom-specific threshold perturbations. These simulation results represent changes in the mean modeled number of symptoms present rather than changes in symptom severity and should be interpreted as exploratory and hypothesis-generating rather than causal or therapeutic effects.