Recursive partitioning analysis model based on 18F-FDG PET/CT metabolic parameters for recurrent or metastatic nasopharyngeal carcinoma treated with PD-1 inhibitors and chemotherapy

We aimed to evaluate the prognostic value of baseline 18 F-FDG PET/CT metabolic parameters and to construct a risk stratification model for recurrent/metastatic nasopharyngeal carcinoma (rmNPC) patients receiving first-line PD-1 inhibitors plus chemotherapy. A total of 148 rmNPC patients from two centers were included. Whole-body total lesion glycolysis (wTLG), whole-body metabolic tumor volume (wMTV), and maximal standardized uptake values (SUV max ) were assessed. Predictive performance was evaluated via receiver operating characteristic (ROC) curve, while progression-free survival (PFS) and overall survival (OS) were identified using Kaplan-Meier and Cox regression analyses. A recursive partitioning analysis (RPA) model was subsequently developed and validated for risk stratification. Lower baseline wTLG and wMTV were associated with better response and longer PFS and OS, with wTLG showing the highest predictive power (AUC = 0.776). The RPA model stratified patients into low-risk (low wTLG), medium-risk (high wTLG without liver metastasis), and high-risk (high wTLG with liver metastasis) groups, demonstrating significantly different median PFS (30.7, 15.9, 7.1 months) and OS (43.7, 29.9, 14.7 months; all p < 0.001), and achieving an AUC of 0.839 for predicting 3-year disease progression. Radiotherapy improved survival outcomes in the low-risk group but not in the high-risk group. Analysis of 213 individual lesions revealed heterogeneous responses, with lymph node metastases showing the best response and liver metastases the poorest. Baseline PET/CT metabolic parameters, particularly wTLG, serve as powerful predictors of survival in NPC patients receiving chemoimmunotherapy. The wTLG-based RPA model provides a robust tool for risk stratification and holds promise for guiding individualized clinical decision-making.