|
|
Building Asian Tumor-patients Prognostic Model with Bayesian Network and SEER Database——Case Study of Non-Small Cell Lung Cancer |
Yin Bincan1, Xin Shichao1, Zhang Han1, Zhao Yuhong1,2() |
1Department of Medical Informatics, China Medical University, Shenyang 110122, China 2Shengjing Hospital of China Medical University, Shenyang 110004, China |
|
|
Abstract [Objective] This study aims to improve the tumor-prognostic assessment for Asian patients who were diagnosed with Non-Small Cell Lung Cancer (NSCLC). The proposed model identifies the influencing factors of the patients’ survival status and predicts their prognostic situation. [Methods] First, we used single factor statistical method and logistic regression to identify the prognostic variables. Second, we employed the Bayesian Network algorithm to construct the prognostic survival model for the Asian NSCLC patients. Finally, we compared the performance of our model with three other algorithms. [Results] The identified prognostic variables include age, tumor size, grade, tumor stage, as well as the lymph nodes ratio. The proposed model could predict NSCLC patients’ prognostic survival status effectively. [Limitations] The SEER database had limited number of prognostic factors, which may influence the prediction accuracy. [Conclusions] The Bayesian Network could help us build optimal prognosis model for cancer patients to improve their survival rates. The proposed model is better than the Decision Tree, Support Vector Machine and Artificial Neural Network models.
|
Received: 31 October 2016
Published: 27 March 2017
|
|
[1] |
National Cancer Institute. SEER Cancer Statistics Review (CSR) 1975-2013 [R/OL]. [2016-09-20]. .
|
[2] |
Ettinger D S, Wood D E, Akerley W, et al.NCCN Guidelines Insights: Non-Small Cell Lung Cancer, Version 4.2016[J]. Journal of the National Comprehensive Cancer Network: JNCCN, 2016, 14(3): 255-264.
pmid: 26957612
|
[3] |
Muers M F, Shevlin P, Brown J.Prognosis in Lung Cancer: Physicians’ Opinions Compared with Outcome and a Predictive Model[J]. Thorax, 1996, 51(9): 894-902.
doi: 10.1136/thx.51.9.894
pmid: 8984699
|
[4] |
Yang L, Takimoto T, Fujimoto J.Prognostic Model for Predicting Overall Survival in Children and Adolescents with Rhabdomyosarcoma[J]. BMC Cancer, 2014, 14: 654. DOI: 10.1186/1471-2407-14-654.
doi: 10.1186/1471-2407-14-654
pmid: 25189734
|
[5] |
Park I, Lee J L, Ryu M H, et al.Prognostic Factors and Predictive Model in Patients with Advanced Biliary Tract Adenocarcinoma Receiving First-line Palliative Chemotherapy[J]. Cancer, 2009, 115(18): 4148-4155.
doi: 10.1002/cncr.24472
pmid: 19536892
|
[6] |
Kim W, Kim K S, Park R W.Nomogram of Naive Bayesian Model for Recurrence Prediction of Breast Cancer[J]. Healthcare Informatics Research, 2016, 22(2): 89-94.
doi: 10.4258/hir.2016.22.2.89
|
[7] |
Kim W, Kim K S, Lee J E, et al.Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine[J]. Journal of Breast Cancer, 2012, 15(2): 230-238.
doi: 10.4048/jbc.2012.15.2.230
|
[8] |
刘雅琴. 乳腺癌患者预后模型的研究[D]. 上海: 上海交通大学, 2008.
|
[8] |
(Liu Yaqin.Study on the Prognosis Model for Breast Cancer [D]. Shanghai: Shanghai Jiaotong University, 2008.)
|
[9] |
Chen Y C, Ke W C, Chiu H W.Risk Classification of Cancer Survival Using ANN with Gene Expression Data from Multiple Laboratories[J]. Computers in Biology and Medicine, 2014, 48: 1-7.
doi: 10.1016/j.compbiomed.2014.02.006
|
[10] |
牟冬梅, 任珂. 三种数据挖掘算法在电子病历知识发现中的比较[J]. 现代图书情报技术, 2016(6): 102-109.
|
[10] |
(Mu Dongmei, Ren Ke.Discovering Knowledge from Electronic Medical Records with Three Data Mining Algorithms[J]. New Technology of Library and Information Service, 2016(6): 102-109.)
|
[11] |
Shin H, Nam Y.A Coupling Approach of a Predictor and a Descriptor for Breast Cancer Prognosis[J]. BMC Medical Genomics, 2014, 7(S1): S4.
doi: 10.1186/1755-8794-7-S1-S4
pmid: 4101306
|
[12] |
American Joint Committee on Cancer, AJCC Cancer Staging Manual[M]. The 7th Edition. New York: Springer Verlag, 2010: 253-270.
|
[13] |
National Comprehensive Cancer Network: NCCN Clinical Practice Guidelines in Oncology: Non-Small Cell Lung Cancer, Version 2.2016 [R/OL]. [2016-09-20]. .
|
[14] |
Hartemink A J.Principled Computational Methods for the Validation and Discovery of Genetic Regulatory Networks [D]. Massachusetts Institute of Technology, 2001: 86-87.
|
[15] |
Kumar Y, Sahoo G.Prediction of Different Types of Liver Diseases Using Rule Based Classification Model[J]. Technology & Health Care Official Journal of the European Society for Engineering & Medicine, 2013, 21(5): 417-432.
|
[16] |
Oh J H, Craft J, Al L R, et al.A Bayesian Network Approach for Modeling Local Failure in Lung Cancer[J]. Physics in Medicine & Biology, 2011, 56(6): 1635-1651.
doi: 10.1088/0031-9155/56/6/008
pmid: 21335651
|
[17] |
张雪雷. 基于禁忌搜索算法的贝叶斯网络在疾病预测与诊断中的应用[D]. 太原: 山西医科大学, 2015.
|
[17] |
(Zhang Xuelei.The Application of Bayesian Network Based on Tabu Search Algorithm in Diseases Prediction and Diagnosis [D]. Taiyuan: Shanxi Medical University, 2015.)
|
[18] |
Lim W L, Wibowo A, Desa M I, et al.A Biogeography-Based Optimization Algorithm Hybridized with Tabu Search for the Quadratic Assignment Problem[J]. Computational Intelligence & Neuroscience, 2016. DOI: 10.1155/2016/5803893.
doi: 10.1155/2016/5803893
pmid: 26819585
|
[19] |
Makond B, Wang K J, Wang K M.Probabilistic Modeling of Short Survivability in Patients with Brain Metastasis from Lung Cancer[J]. Computer Methods & Programs in Biomedicine, 2015, 119(3): 142-162.
doi: 10.1016/j.cmpb.2015.02.005
pmid: 25804445
|
[20] |
魏珍, 张雪雷, 饶华祥, 等.禁忌搜索算法的贝叶斯网络模型在冠心病影响因素分析中的应用[J].中华流行病学杂志, 2016, 37(6): 895-899.
doi: 10.3760/cma.j.issn.0254-6450.2016.06.031
|
[20] |
(Wei Zhen, Zhang Xuelei, Rao Huaxiang, et al.Using the Tabu-search-algorithm-based Bayesian Network to Analyze the Risk Factors of Coronary Heart Diseases[J]. Chinese Journal of Epidemiology, 2016, 37(6): 895-899.)
doi: 10.3760/cma.j.issn.0254-6450.2016.06.031
|
[21] |
杨乔, 张俊萍. 肿瘤登记数据库的临床应用[J]. 循证医学, 2013, 13(4): 250-251, 256.
doi: 10.3969/j.issn.1671-5144.2013.04.016
|
[21] |
(Yang Qiao, Zhang Junping.Clinical Applications of the Tumor Registry Database[J]. The Journal of Evidence-Based Medicine, 2013, 13(4): 250-251, 256.)
doi: 10.3969/j.issn.1671-5144.2013.04.016
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
|
Shared |
|
|
|
|
|
Discussed |
|
|
|
|