中德临床肿瘤学杂志(英文版)2006，Vol.5Issue(1)：8-12,5.
应用支持向量机方法对鼻咽癌治疗后5年生存状态的预测
Application of Support Vector Machine to Predict 5-year Survival Status of Patients with Nasopharyngeal Carcinoma after Treatment
华贻军 1余舒 2洪明晃 1杨晓伟 2邱枋 1郭灵 1黄培钰 1张国义3
作者信息
- 1. Department of Nasopharyngeal Carcinoma, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China
- 2. Department of Mathematics, South China University of Technology, Guangzhou 510641, China
- 3. Department of Radiation, Cancer Center, Sun Yat-sen University, Guangzhou 510060, China
- 折叠
摘要
Abstract
Objective: Support Vector Machine (SVM) is a machine-learning method, based on the principle of structural risk minimization, which performs well when applied to data outside the training set. In this paper, SVM was applied to predict 5-year survival status of patients with nasopharyngeal carcinoma (NPC) after treatment, we expect to find a new way for prognosis studies in cancer so as to assist right clinical decision for individual patient. Methods: Two modelling methods were used in the study; SVM network and a standard parametric logistic regression were used to model 5-year survival status. And the two methods were compared on a prospective set of patients not used in model construction via receiver operating characteristic (ROC) curve analysis. Results: The SVM1, trained with the 25 original input variables without screening, yielded a ROC area of 0.868, at sensitivity to mortality of 79.2% and the specificity of 94.5%. Similarly, the SVM2, trained with 9 input variables which were obtained by optimal input variable selection from the 25 original variables by logistic regression screening, yielded a ROC area of 0.874, at a sensitivity to mortality of 79.2% and the specificity of 95.6%, while the logistic regression yielded a ROC area of 0.751 at a sensitivity to mortality of 66.7% and gave a specificity of 83.5%. Conclusion: SVM found a strong pattern in the database predictive of 5-year survival status. The logistic regression produces somewhat similar, but better, results. These results show that the SVM models have the potential to predict individual patient's 5-year survival status after treatment, and to assist the clinicians for making a good clinical decision.关键词
support vector machine/logistic regression/nasopharyngeal carcinoma/predictive model/radiotherapy/ROC curveKey words
support vector machine/logistic regression/nasopharyngeal carcinoma/predictive model/radiotherapy/ROC curve分类
医药卫生引用本文复制引用
华贻军,余舒,洪明晃,杨晓伟,邱枋,郭灵,黄培钰,张国义..应用支持向量机方法对鼻咽癌治疗后5年生存状态的预测[J].中德临床肿瘤学杂志(英文版),2006,5(1):8-12,5.
