Clinical Research Papers:
Development of a ten-signature classifier using a support vector machine integrated approach to subdivide the M1 stage into M1a and M1b stages of nasopharyngeal carcinoma with synchronous metastases to better predict patients’ survival
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Rou Jiang1,2,3,*, Rui You1,2,*, Xiao-Qing Pei4,*, Xiong Zou1,2,*, Meng-Xia Zhang1,2, Tong-Min Wang2, Rui Sun1,2, Dong-Hua Luo1,2, Pei-Yu Huang1,2, Qiu-Yan Chen1,2, Yi-Jun Hua1,2, Lin-Quan Tang1,2, Ling Guo1,2, Hao-Yuan Mo1,2, Chao-Nan Qian1,2, Hai-Qiang Mai1,2, Ming-Huang Hong2,5, Hong-Min Cai6 and Ming-Yuan Chen1,2
1 Department of Nasopharyngeal Carcinoma, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
2 Collaborative Innovation Center for Cancer Medicine, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
3 Department of Cancer Prevention, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
4 Department of Ultrasonography, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
5 Department of Clinical Trials Center, Sun Yat-sen University Cancer Center, Guangzhou, P. R. China
6 School of Computer and Engineering, South China University of Technology, Guangzhou, P. R. China
* These authors have contributed equally to this study
Ming-Yuan Chen, email:
Hong-Min Cai, email:
Keywords: nasopharyngeal carcinoma, synchronous metastases, support vector machine, prognosis, therapy
Received: August 04, 2015 Accepted: November 16, 2015 Published: November 30, 2015
The aim of this study was to develop a prognostic classifier and subdivided the M1 stage for nasopharyngeal carcinoma patients with synchronous metastases (mNPC). A retrospective cohort of 347 mNPC patients was recruited between January 2000 and December 2010. Thirty hematological markers and 11 clinical characteristics were collected, and the association of these factors with overall survival (OS) was evaluated. Advanced machine learning schemes of a support vector machine (SVM) were used to select a subset of highly informative factors and to construct a prognostic model (mNPC-SVM). The mNPC-SVM classifier identified ten informative variables, including three clinical indexes and seven hematological markers. The median survival time for low-risk patients (M1a) as identified by the mNPC-SVM classifier was 38.0 months, and survival time was dramatically reduced to 13.8 months for high-risk patients (M1b) (P < 0.001). Multivariate adjustment using prognostic factors revealed that the mNPC-SVM classifier remained a powerful predictor of OS (M1a vs. M1b, hazard ratio, 3.45; 95% CI, 2.59 to 4.60, P < 0.001). Moreover, combination treatment of systemic chemotherapy and loco-regional radiotherapy was associated with significantly better survival outcomes than chemotherapy alone (the 5-year OS, 47.0% vs. 10.0%, P < 0.001) in the M1a subgroup but not in the M1b subgroup (12.0% vs. 3.0%, P = 0.101). These findings were validated by a separate cohort. In conclusion, the newly developed mNPC-SVM classifier led to more precise risk definitions that offer a promising subdivision of the M1 stage and individualized selection for future therapeutic regimens in mNPC patients.
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