Research Papers:

Establishment of an integrated model incorporating standardised uptake value and N-classification for predicting metastasis in nasopharyngeal carcinoma

Yuan Zhang, Wen-Fei Li, Yan-Ping Mao, Guan-Qun Zhou, Hao Peng, Ying Sun, Qing Liu, Lei Chen and Jun Ma _

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Oncotarget. 2016; 7:13612-13620. https://doi.org/10.18632/oncotarget.7253

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Yuan Zhang1,*, Wen-Fei Li1,*, Yan-Ping Mao1, Guan-Qun Zhou1, Hao Peng1, Ying Sun1, Qing Liu2, Lei Chen1, Jun Ma1

1Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Canton, Guangdong, China

2Department of Medical Statistics and Epidemiology, School of Public Health, Sun Yat-sen University, Canton, Guangdong, China

*These authors have contributed equally to this work

Correspondence to:

Jun Ma, e-mail: [email protected]

Lei Chen, e-mail: [email protected]

Keywords: nasopharyngeal neoplasms, metastasis, TNM staging, maximum standardized uptake value, recursive partitioning analysis

Received: October 28, 2015     Accepted: January 27, 2016     Published: February 8, 2016


Background: Previous studies reported a correlation between the maximum standardised uptake value (SUVmax) obtained by 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography (PET) and distant metastasis in nasopharyngeal carcinoma (NPC). However, an integrated model incorporating SUVmax and anatomic staging for stratifying metastasis risk has not been reported.

Results: The median SUVmax for primary tumour (SUV-T) and cervical lymph nodes (SUV-N) was 13.6 (range, 2.2 to 39.3) and 8.4 (range, 2.6 to 40.9), respectively. SUV-T (HR, 3.396; 95% CI, 1.451-7.947; P = 0.005), SUV-N (HR, 2.688; 95%CI, 1.250-5.781; P = 0.011) and N-classification (HR, 2.570; 95%CI, 1.422-4.579; P = 0.001) were identified as independent predictors for DMFS from multivariate analysis. Three valid risk groups were derived by RPA: low risk (N0-1 + SUV-T <10.45), medium risk (N0-1 + SUV-T >10.45) and high risk (N2-3). The three risk groups contained 100 (22.3%), 226 (50.3%), and 123 (27.4%) patients, respectively, with corresponding 3-year DMFS rates of 99.0%, 91.5%, and 77.5% (P <0.001). Moreover, multivariate analysis confirmed the RPA-based prognostic grouping as the only significant prognostic indicator for DMFS (HR, 3.090; 95%CI, 1.975-4.835; P <0.001).

Methods: Data from 449 patients with with histologically-confirmed, stage I-IVB NPC treated with radiotherapy or chemoradiotherapy were retrospectively analysed. A prognostic model for distant metastasis-free survival (DMFS) was derived by recursive partitioning analysis (RPA) combining independent predictors identified by multivariate analysis.

Conclusion: SUV-T, SUV-N and N-classification were identified as independent predictors for DMFS. An integrated RPA-based prognostic model for DMFS incorporating SUV-N and N-classification was proposed.

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