Early Target Identification for Symptomatic Intervention in Radiotherapy in Patients with Esophageal Cancer: A Longitudinal Cross-lagged Panel Dynamic Network Analysis

Background Predictive identification of core symptoms in patients with esophageal cancer undergoing radiotherapy is essential for early symptom prevention. Few studies address dynamic symptom prediction using longitudinal network analysis. Objectives This study aimed to establish predictive relationships between symptoms and identify early intervention targets by constructing a dynamic symptom network. Methods This prospective longitudinal study enrolled 146 patients from June 2023 to May 2024. Data were collected using the M.D. Anderson Symptom Inventory Gastrointestinal Cancer Module and the Hospital Anxiety and Depression Scale at three points: T0(pre-radiotherapy), T1(mid-radiotherapy), and T2(1-month post-radiotherapy). R software was used to construct cross-lagged panel networks and calculate predictive and centrality indices. Results Distress had the most substantial outgoing influence. At T0→T1, distress predicted anxiety, depression, and disturbed sleep. At T1→T2, distress predicted loss of appetite. Anxiety and depression were most affected at T0→T1, while disturbed sleep and loss of appetite were most affected at T1→T2. Dry mouth and disturbed sleep were significantly aggravated at T1, with dry mouth showing the strongest bridging effect at T0→T1. Drowsiness had the highest centrality at T1→T2. Conclusions Distress should be targeted for early intervention to reduce anxiety and depression and improve sleep quality and physical condition post-radiotherapy. Clinicians should dynamically manage dry mouth and drowsiness during radiotherapy to prevent symptom crosstalk. Implications for cancer survivorship Managing psycho-emotional states before radiotherapy is critical to preventing other symptoms. Active pharmacologic and non-pharmacologic interventions are needed to improve dry mouth and drowsiness during radiotherapy.