M1 stage subdivisions based on F-FDG PET-CT parameters to identify locoregional radiotherapy for metastatic nasopharyngeal carcinoma

Abstract

Purpose: To establish a risk classification of de novo metastatic nasopharyngeal carcinoma (mNPC) patients based on 18 F-fluorodeoxyglucose positron emission tomography/computed tomography ( 18 F-FDG PET-CT) radiomics parameters to identify suitable candidates for locoregional radiotherapy (LRRT). Methods: In all, 586 de novo mNPC patients who underwent 18 F-FDG PET-CT prior to palliative chemotherapy (PCT) were involved. A Cox regression model was performed to identify prognostic factors for overall survival (OS). Candidate PET-CT parameters were incorporated into the PET-CT parameter score (PPS). Recursive partitioning analysis (RPA) was applied to construct a risk stratification system. Results: Multivariate Cox regression analyses revealed that total lesion glycolysis of locoregional lesions (LRL-TLG), the number of bone metastases (BMs), metabolic tumor volume of distant soft tissue metastases (DSTM-MTV), pretreatment Epstein–Barr virus DNA (EBV DNA), and liver involvement were independent prognosticators for OS. The number of BMs, LRL-TLG, and DSTM-MTV were incorporated as the PPS. Eligible patients were divided into three stages by the RPA-risk stratification model: M1a (low risk, PPS low + no liver involvement), M1b (intermediate risk, PPS low + liver involvement, PPS high + low EBV DNA), and M1c (high risk, PPS high + high EBV DNA). PCT followed by LRRT displayed favorable OS rates compared to PCT alone in M1a patients ( p 0.05). Conclusions: The PPS-based RPA stratification model could identify suitable candidates for LRRT. Patients with stage M1a disease could benefit from LRRT.