Risk prediction of prognosis in patients with colorectal cancer under radical operation based on support vector machine

Du Chengfeng; Hu Min; He Jingjing; Ming Xin; Deng Dan

doi:10.13406/j.cnki.cyxb.001086

您当前的位置：

首页 >

文章列表页 >

Risk prediction of prognosis in patients with colorectal cancer under radical operation based on support vector machine

更新时间：2024-01-17

- Risk prediction of prognosis in patients with colorectal cancer under radical operation based on support vector machine
- Journal of Chongqing Medical University Vol. 42, Issue 2, Pages: 213-218(2017)
- 作者机构：
  
  重庆医科大学公共卫生与管理学院卫生统计与信息管理教研室
- 作者简介：
- 基金信息：
- DOI：10.13406/j.cnki.cyxb.001086
  CLC： R735.34
- Published：2017，
扫描看全文
Du Chengfeng, Hu Min, He Jingjing, et al. Risk prediction of prognosis in patients with colorectal cancer under radical operation based on support vector machine. [J]. Journal of Chongqing Medical University 42(2):213-218(2017)
DOI：

Du Chengfeng, Hu Min, He Jingjing, et al. Risk prediction of prognosis in patients with colorectal cancer under radical operation based on support vector machine. [J]. Journal of Chongqing Medical University 42(2):213-218(2017) DOI： 10.13406/j.cnki.cyxb.001086.

摘要

目的:基于支持向量机（support vector machine

SVM）预测结直肠癌患者术后风险。方法:回顾性分析2011年1月至2013年12月行根治性手术治疗的199例结直肠癌患者临床资料

按照是否无病生存（disease free survival

DFS）分为预后好、差2组。采用SAS9.2进行基本统计分析、logistic回归分析

运用R3.2.4软件建立SVM预测模型、受试者工作曲线（receiver operating characteristic curve

ROC）及曲线下面积（area under the curve

AUC）评价模型预测效果。结果:差异性分析显示

总蛋白、白蛋白、阳性淋巴率、组织类型、p53、N分期、放化疗等因素在预后组间存在统计学差异（P

0.05）;多因素逐步logistic回归结果显示

N2分期（OR=13.5

95%CI=3.85

7.5）、p53阳性（OR=0.314

95%CI=0.125

.790）可能是影响预后的危险因素。线性核、多项式核、sigmoid核及RBF（径向基函数）核SVM模型的预测准确率分别为69.70%、66.70%、69.70%、74.24%。对所有预测模型性能对比显示

SVM模型预测性能高于logistic回归模型

其中RBF-SVM模型效果最好

预测准确率为74.24%

特异度为0.714

灵敏度为0.765

AUC值为0.708（95%CI=0.551

.866）。结论:N2分期、p53阳性可能是影响预后结局的危险因素

应当予以重视

减少预后风险。

Abstract

Objective:To predict the postoperative risk of patients with colorectal cancer based on support vector machine（SVM）.Methods:Clinical data of 199 patients with colorectal cancer were collected retrospectively from the hospital from January2011 to December 2013

and patients were divided into two groups according to disease free survival or not. Basic statistics analysis and logistic regression were conducted by SAS9.2. Parameters optimization and establishment of SVM model wereanalyzed by R3.2.4. Receiver operating characteristic curve（ROC）and area under the curve（AUC）were used to evaluate their performance. Results:Differences analysis showed that total protein

albumin

positive lymph node number

tissue type

p53

radiotherapy and chemo-therapy were significantly different between prognosis groups（P<0.05）. Multiple logistic regression showed that N2（OR=13.5

95%CI=3.85 to 47.5）

p53 positive（OR=0.314

95%CI=0.125 to 0.790）maybe the risk factors of prognosis. The prediction accuracy of linear-kernel

polynomial-kernel

sigmoid-kernel and RBF（radial basis function）-kernel was 69.70%

66.70%

69.70%

74.24%

respectively. The results of performance comparison showed that SVM models performed better than logistic regression model;RBF-SVM was the best among all the models

with the prediction accuracy of 74.24%;sensitivity

specificity and AUC were 0.714

0.765

0.708（95%CI=0.551 to 0.866）

respectively. Conclusion:N2 staging and p53 positive maybe the risk factors for prognosis and more attention should be paid to decrease the risk of prognosis.

关键词

结直肠癌风险预测支持向量机

Keywords

colorectal cancerRisk predictionsupport vector machine

references

Views

228

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Identification of shared genetic features and molecular mechanisms of metabolic syndrome and colorectal cancer based on transcriptomic data

Association of antibiotics with tumor metastasis in colorectal cancer patients undergoing 5-fluorouracil treatment

Role of N⁶-methyladenosine mediated by methyltransferase-like 3 in oxaliplatin resistance of colorectal cancer

Adherence of free colonoscopy screening and its influencing factors among high-risk populations of colorectal cancer in Chongqing

PIASy expresses abnormally in human colorectal cancer tissues and affects the apoptosis and cycle of colorectal cancer cells

Related Author

Zhang Yuan

Xie Biao

Zhong Xiaoni

Zheng Wei

Zhang Cong

Li Kangjie

Deng Jielian

Yin Qi

Related Institution

Department of Cardiology,The First Affiliated Hospital of Chongqing Medical University

Department of Epidemiology and Health Statistics, School of Public Health, Chongqing Medical University

Research Center for Medicine and Social Development,School of Public Health,Chongqing Medical University

Department of Health Laboratory Technology,School of Public Health,Chongqing Medical University

Department of Clinical Laboratory，The Second Hospital of Shandong University/Tumor Marker Detection Engineering Laboratory of Shandong Province

⁰