Establishment and validation of a recursive partitioning analysis based prognostic model for guiding re-radiotherapy in locally recurrent nasopharyngeal carcinoma patients.

Objective: In this study, we aimed to establish and validate an integrated prognostic model for locally recurrent nasopharyngeal carcinoma (lrNPC) patients, and evaluate the benefit of re-radiotherapy (re-RT) in patients with different risk levels.

Materials And Methods: In total, 531 patients with lrNPC were retrospectively reviewed in this study, including 271 patients from 2006 to 2012 as the training cohort and 260 patients from 2013 to 2016 as the validation cohort. 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 multivariate 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). The results of risk stratification and re-RT efficacy were verified in the validation cohort.

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.