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支持向量机预测食管鳞癌患者术后生存期

张天, 晁玮霞, 赵云岗, 王明, 王广超, 贺红梅, 刘瑞敏, 魏书堂, 韩大正, 朱晗, 马瑾, 谷娟, 马远方, 齐义军

张天, 晁玮霞, 赵云岗, 王明, 王广超, 贺红梅, 刘瑞敏, 魏书堂, 韩大正, 朱晗, 马瑾, 谷娟, 马远方, 齐义军. 支持向量机预测食管鳞癌患者术后生存期[J]. 肿瘤防治研究, 2015, 42(08): 765-771. DOI: 10.3971/j.issn.1000-8578.2015.08.004
引用本文: 张天, 晁玮霞, 赵云岗, 王明, 王广超, 贺红梅, 刘瑞敏, 魏书堂, 韩大正, 朱晗, 马瑾, 谷娟, 马远方, 齐义军. 支持向量机预测食管鳞癌患者术后生存期[J]. 肿瘤防治研究, 2015, 42(08): 765-771. DOI: 10.3971/j.issn.1000-8578.2015.08.004
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ZHANG Tian, CHAO Weixia, ZHAO Yungang, WANG Ming, WANG Guangchao, HE Hongmei, LIU Ruimin, WEI Shutang, HAN Dazheng, ZHU Han, MA Jin, GU Juan, MA Yuanfang, QI Yijun. Support Vector Machine Predicts Survival of Esophageal Squamous Cell Carcinoma Patients[J]. Cancer Research on Prevention and Treatment, 2015, 42(08): 765-771. DOI: 10.3971/j.issn.1000-8578.2015.08.004
Citation: ZHANG Tian, CHAO Weixia, ZHAO Yungang, WANG Ming, WANG Guangchao, HE Hongmei, LIU Ruimin, WEI Shutang, HAN Dazheng, ZHU Han, MA Jin, GU Juan, MA Yuanfang, QI Yijun. Support Vector Machine Predicts Survival of Esophageal Squamous Cell Carcinoma Patients[J]. Cancer Research on Prevention and Treatment, 2015, 42(08): 765-771. DOI: 10.3971/j.issn.1000-8578.2015.08.004
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支持向量机预测食管鳞癌患者术后生存期

  • 1. 475004 开封，河南大学医学院细胞与分子免疫学重点实验室；2. 475004 开封，河南大学医学院预防医学教研室；3. 475004 开封，河南大学第一附属医院消化内科

基金项目: 国家自然科学基金（30700366，81072039）；河南大学研究生科研创新专项资助（Y1326089）
详细信息
    作者简介:

    张天（1990-），男，硕士，主要从事食管癌肿瘤的研究

    通讯作者:

    马远方，E-mail: mayf@henu.edu.cn

    齐义军，E-mail: qiyijun@hotmail.com

  • 中图分类号: R735.1

Support Vector Machine Predicts Survival of Esophageal Squamous Cell Carcinoma Patients

  • 1. Key Laboratory of Cellular and Molecular Immunology, College of Medicine, He'nan University, Kaifeng 475004, China; 2. Department of Preventive Medicine, He'nan University, Kaifeng 475004, China; 3. Department of Digestive Medicine, The First Affiliated Hospital of He'nan University, Kaifeng 475000, China

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    摘要
  • 摘要: 目的 应用支持向量机（support vector machine, SVM）建立食管鳞状细胞癌（esophageal squamous cell carcinoma, ESCC）术后生存期预测模型并评估该模型判断ESCC生存期的效能。方法 随访168例接受根治性手术治疗的ESCC患者，分析ESCC临床病理特征和14-3-3σ、热休克蛋白gp96、巨噬细胞移动抑制因子（migrationinhibitory factor, MIF）等3个蛋白的表达规律与ESCC生存期的相关性；应用Matlab软件进行SVM运算，对训练组128例ESCC患者建立最优预后分类模型ESCC-SVM，并用测试组40例患者验证分类效率，ROC曲线分析ESCC-SVM及其他预后相关因子对高低死亡风险ESCC的识别能力。结果 ESCC-SVM由性别、T分期、组织学分级、淋巴结转移、TNM分期、14-3-3σ和gp96等7个最优属性组成，该模型区分训练组和测试组ESCC五年整体生存率的最大AUC分别为0.96、0.86、准确率分别为97.7%、90.0%，明显优于目前临床应用的TNM分期（准确率分别为62.5%、67.5%）及其他各临床病理属性。Cox多因素比例风险回归模型分析发现年龄、T分期、gp96和ESCC-SVM是影响ESCC术后生存期的独立因素。ESCC-SVM与性别、T分期、组织学分级、淋巴结转移、TNM分期和14-3-3σ均显著相关。结论 本研究建立的ESCC-SVM为预后评估、临床治疗方案选择及个体化治疗提供了理论依据。

     

    Abstract: Objective To develop an esophageal squamous cell carcinoma-support vector machine(ESCCSVM) classifier for ESCC survival prediction, and to evaluate its performance of prognostic prediction. Methods Based on the survival data of 168 ESCC patients, we determined the correlation between ESCC survival and clinicopathological features, the expression levels of three biomarkers, 14-3-3σ, heatshock protein gp96 and macrophage migration inhibitory factor(MIF). The SVM algorithm performing in Matlab was used to develop an optimal ESCC-SVM classifier for prognostic prediction by using 128 ESCC patients randomly selected out of 168 patients as the training set and the rest 40 cases as a test set to verify the classification efficiency. Receiver operating characteristic(ROC) curve analysis was used to evaluate the potential of ESCC-SVM classifier and other prognostic factors for identification of high- and low-risk patients. Results ESCC-SVM classifier comprised sex, T stage, histological grade, lymph node metastasis, TNM stage, 14-3-3σ and gp96 as the optimal factors. ROC curve analysis showed that ESCC-SVM produced the largest AUC both in the training and validation groups (0.96, 0.86, respectively), with an accuracy of 97.7% and 90.0%, respectively, which performed significantly better than TNM system(accuracy 62.5%, 67.5%, respectively) and other clinicopathological features. SVM classifier was significantly correlated with sex, T stage, histological grade, lymph node metastasis, TNM stage and 14-3-3σ. Multivariate Cox proportional hazards regression analysis indicated that age, T stage, gp96 and ESCC-SVM were independent prognostic factors of ESCC. Conclusion The seven-feature SVM classifier for ESCC prognosis prediction would provide theoretical evidence for the prognosis, clinical treatment selection and individualized treatment.

     

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出版历程
  • 收稿日期:  2014-08-03
  • 修回日期:  2014-12-28
  • 刊出日期:  2015-08-24

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