Establishment of an integrated model for predicting survival and guiding treatment in local recurrence nasopharyngeal carcinoma

Objective: In this study, we aimed to establish an integrated prognostic model for local recurrence nasopharyngeal carcinoma (lrNPC) patients, and evaluate the benefit of re-radiotherapy (RT) in patients with different risk levels. Materials: and methods: In total, 271 patients with lrNPC were retrospectively reviewed in this study. Overall survival (OS) was the primary endpoint. Multivariate analysis was performed to select the significant prognostic factors (P<0.05). A prognostic model for OS was derived by recursive partitioning analysis (RPA) combining independent predictors using the algorithm of optimized binary partition. Results: : Three independent prognostic factors (age, relapsed T [rT] stage, and Epstein-Barr virus [EBV] DNA) were identified from multivariable analysis. Five prognostic groups were derived from an RPA model that combined rT stage and EBV DNA. After further pair-wise comparisons of survival outcome in each group, three risk groups were generated. We investigated the role of re-RT in different risk groups, and found that re-RT could benefit patients in the low (P<0.001) and intermediate-risk subgroups (P=0.017), while no association between re-RT and survival benefit was found in the high-risk subgroup (P=0.328). Conclusion: Age, rT stage and EBV DNA were identified as independent predictors for lrNPC. We established an integrated RPA-based prognostic model for OS incorporating rT stage and EBV DNA, which could guide individual treatment for lrNPC.