Purpose: Despite advancements in multidisciplinary treatment strategies, long-term survival of esophageal squamous cell carcinoma (ESCC) patients remain suboptimal. Identifying novel prognostic biomarkers is essential for individualized treatment and surveillance approaches. The Advanced Lung Cancer Inflammation Index (ALI), an integrative biomarker reflecting nutritional, inflammatory, and immune status, has emerged as a potential predictor of prognosis. However, its association with overall survival (OS) in non-surgical ESCC patients remains poorly understood. This study aims to evaluate the prognostic value of ALI in ESCC patients undergoing radical radiotherapy and to develop a predictive nomogram to support clinical decision-making.
Patients and methods: We retrospectively analyzed pre-radiotherapy ALI values of 266 ESCC patients treated from January 2017 to October 2022. A restricted cubic spline (RCS) model explored the link between continuous ALI levels and survival risk. Univariate and multivariate Cox proportional hazards models were used to identify independent predictors of OS, and a nomogram was constructed to predict 1 - year, 3 - year, and 5 - year OS probabilities.
Results: RCS analysis stratified patients into low (≤227.5), medium (227.5-570.4), and high (>570.4) ALI risk groups. Kaplan-Meier curves showed significant differences among the three groups, with lower ALI values associated with poorer prognosis (P = 0.0057). Multivariate analysis confirmed that ALI, radiation dose, T stage, and N stage were independent predictors of OS. A forest plot precisely quantified each variable's prognostic contribution. The developed nomogram exhibited moderate to high predictive accuracy, as reflected by the area under the time-dependent ROC curves. Decision curve analysis (DCA) indicated a net clinical benefit at 1 - year, 3 - year, and 5 - year time points.
Conclusion: ALI is an independent prognostic factor for overall survival in non-surgical ESCC patients treated with radical radiotherapy. The ALI-based nomogram demonstrates good predictive performance and may serve as a useful tool for personalized risk stratification and clinical management.
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