Abstract

BackgroundThere is no clinically applicable prognostic model designed for patients with de novo metastatic nasopharyngeal carcinoma (mNPC) treated with chemotherapy followed by locoregional radiotherapy (LRRT). We sought to develop a predictive tool of overall survival for individualized prediction and risk stratification in this heterogeneous patient population.Patients and methodsA total of 244 eligible patients with de novo mNPC, who were treated with platinum-based first-line chemotherapy followed by LRRT, were included in this retrospective study. We divided patients into the training and validation sets based on the date of initial treatment, with 152 patients treated between 2008 and 2013 comprising the training set for model development and 92 patients treated at a later time (2014 to 2015) forming the validation set. We applied Cox proportional hazards model to examine factors associated with overall survival (OS). We developed and subsequently validated a prognostic model to predict OS. We assessed the performance of this prognostic model and stratified patients based on prognostic scores obtained from this proposed model.ResultsThe median OS of the entire cohort was 60.9 months. C-creative protein, number of metastatic sites, liver metastasis, post-treatment Epstein–Barr virus DNA, and response of metastasis were significantly associated with OS. A prognostic model for individual survival prediction was developed and graphically represented as a nomogram. The model showed favorable discrimination (C-index: 0.759), predictive accuracy [time dependent area under the curve (tAUC) at 5 years: 0.800], and calibration, and was further validated in an independent dataset. A risk stratification derived from the model can stratify these patients into three prognostic subgroups with significantly different survival.ConclusionWe developed and validated a prognostic model that exhibited adequate performance in individualized prediction and risk stratification for patients with de novo mNPC treated with chemotherapy followed by LRRT.

Highlights

  • Nasopharyngeal carcinoma (NPC) is endemic in southern China, Southeast Asia, and North Africa, whereas its incidenceVolume 6 - Issue 1 - 2021 is drastically lower in other regions

  • Survival curves stratified based on the three strata showed significant survival differences both in the training and validation sets. This is the first study to investigate a robust prognostic tool to generate individualized predictions and risk stratifications regarding the prognosis of patients with de novo metastatic nasopharyngeal carcinoma (mNPC) who were treated with chemotherapy followed by locoregional radiotherapy (LRRT)

  • Given that there is no specific model designed for stratifying ideal LRRT candidates in patients with de novo mNPC, the current prognostic model could serve as a clinically useful tool to make individualized treatment recommendations in this heterogeneous patient population

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Summary

Introduction

Nasopharyngeal carcinoma (NPC) is endemic in southern China, Southeast Asia, and North Africa, whereas its incidenceVolume 6 - Issue 1 - 2021 is drastically lower in other regions. Platinum-based chemotherapy is generally regarded as the primary treatment of patients with de novo metastatic NPC, with a response rate of 70%-80%.4-6. There is no clinically applicable prognostic model designed for patients with de novo metastatic nasopharyngeal carcinoma (mNPC) treated with chemotherapy followed by locoregional radiotherapy (LRRT). Patients and methods: A total of 244 eligible patients with de novo mNPC, who were treated with platinum-based firstline chemotherapy followed by LRRT, were included in this retrospective study. A risk stratification derived from the model can stratify these patients into three prognostic subgroups with significantly different survival. Conclusion: We developed and validated a prognostic model that exhibited adequate performance in individualized prediction and risk stratification for patients with de novo mNPC treated with chemotherapy followed by LRRT.

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