Prognostic value of lymph node-to-primary tumor ratio of PET standardized uptake value for nasopharyngeal carcinoma: a recursive partitioning risk stratification analysis.

Abstract

Induction chemotherapy (IC) combined with concurrent chemoradiotherapy has become the standard treatment for locoregionally advanced nasopharyngeal carcinoma (LA-NPC). Data on the prognostic value of the lymph node-to-primary tumor ratio (NTR) of positron emission tomography (PET) standardized uptake value (SUV) for patients treated with IC were limited. To evaluate the prognostic value of the SUV NTR for patients with LA-NPC treated with IC. In all, 467 patients with pretreatment 18F-fluorodeoxyglucose PET/computed tomography (CT) scans between September 2017 and November 2020 were retrospectively reviewed. The receiver operating characteristic (ROC) analysis was used to determine the optimal cut-off value of SUV NTR. Kaplan-Meier method was used to evaluate survival rates. The recursive partitioning analysis (RPA) was performed to construct a risk stratification model. The optimal cutoff value of SUV NTR was 0.74. Multivariate analyses showed that SUV NTR and overall stage were independent predictors for distant metastasis-free survival (DMFS) and regional recurrent-free survival (RRFS). Therefore, an RPA model based on the endpoint of DMFS was generated and categorized the patients into three distinct risk groups: RPA I (low risk: SUV NTR < 0.74 and stage III), RPA II (medium risk: SUV NTR < 0.74 and stage IVa, or SUV NTR ⩾ 0.74 and stage III), and RPA III (high risk: SUV NTR ⩾ 0.74 and stage IVa), with a 3-year DMFS of 98.9%, 93.4%, and 84.2%, respectively. ROC analysis showed that the RPA model had superior predictive efficacy than the SUV NTR or overall stage alone. SUV NTR was an independent prognosticator for distant metastasis and regional recurrence in locoregionally advanced NPC. The RPA risk stratification model based on SUV NTR provides improved DMFS and RRFS prediction over the eighth edition of the TNM (Tumor Node Metastasis) staging system.