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The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer

Received: 6 April 2016     Published: 7 April 2016
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Abstract

In this paper, to build a predictive model of hepatitis B virus (HBV) reactivation in primary liver cancer (PLC) patients after precise radiotherapy (RT). Logistic regression analysis was adopted to extract the optimal feature subset, TNM, HBV DNA level and outer margin of RT were risk factors for HBV reactivation (P < 0.05). A predictive model of support vector machine (SVM) was established for the optimal feature subset and all of PLC data sets. The experimental results proved that the former obviously improves the classification accuracy, which increased from 74.44% to 78.89%. In this paper, it is concluded that TNM, HBV DNA levels and outer boundary are the risk factor for HBV reactivation (P < 0.05).

Published in Journal of Electrical and Electronic Engineering (Volume 4, Issue 2)
DOI 10.11648/j.jeee.20160402.15
Page(s) 31-34
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Primary Liver Cancer, Data Set, Feature Extraction, Support Vector Machine (SVM)

References
[1] Lok ASF, Lai CL, Wu PC, et al. Hepatitis B virus infection in Chinese families in Hong Kong. Am J Epidemiol, 1987, 126(3): 492-499.
[2] Yao Hui Gong Jinlan, lily, tinna. Accurate liver cancer patients after radiotherapy of HBV virus reactivation risk factor analysis [J]. Journal of cancer, 2014, 29 (6): 675-677.
[3] Yang Binghui. Primary liver cancer / / China anti-cancer association. New standard of diagnosis and treatment of common malignant tumors. Beijing: Beijing union medical university press, 1999: 389-479.
[4] Tamori A, Nishiguchi S, Tanaka M, et al. Lamivudine therapy for hepatitis B virus reactivation in a patient receiving intra-arterial chemotherapy for advanced hepatocellular carcinoma. Hepatol Res, 2003, 26(1): 77-80.
[5] Jang JW, Choi JY, Bae SH, et al. Transarterial chemolipiodolization can reactivate hepatitis B virus replication in patients with hepatocellular carcinoma. J Hepatol, 2004, 41(3): 427-435.
[6] Jang JW, Choi JY, Bae SH, et al. A randomized controlled study of preemptive lamivudine in patients receiving transarterial chemo-lipiodolization. Hepatology, 2006, 43(2): 233-240.
[7] Xiao-an wu Zhang Zhi Yong, hong, saving, etc. The three dimensional conformal radiotherapy in the treatment of 86 cases of liver cancer clinical efficacy analysis. Journal of cancer, 2007, 22(4): 373-375.
[8] Huangwei, Zhangwei, Fanmin et al. Risk factors for hepatitis B virus reactivation after conformal radiotherapy in patients with hepatocellular carcinoma. Cancer Science, 2014, 193-197.
[9] Zhangtian Zhao Yungang, wang Ming, etc Support vector machine (SVM) to predict esophageal squamous carcinoma postoperative survival. Cancer prevention and control research. 2015, 765-771.
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Cite This Article
  • APA Style

    Wang Shuai, Wu Guan-peng, Huang Wei, Liu Tong-hai, Yin Yong, et al. (2016). The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer. Journal of Electrical and Electronic Engineering, 4(2), 31-34. https://doi.org/10.11648/j.jeee.20160402.15

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    ACS Style

    Wang Shuai; Wu Guan-peng; Huang Wei; Liu Tong-hai; Yin Yong, et al. The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer. J. Electr. Electron. Eng. 2016, 4(2), 31-34. doi: 10.11648/j.jeee.20160402.15

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    AMA Style

    Wang Shuai, Wu Guan-peng, Huang Wei, Liu Tong-hai, Yin Yong, et al. The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer. J Electr Electron Eng. 2016;4(2):31-34. doi: 10.11648/j.jeee.20160402.15

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  • @article{10.11648/j.jeee.20160402.15,
      author = {Wang Shuai and Wu Guan-peng and Huang Wei and Liu Tong-hai and Yin Yong and Liu Yi-hui},
      title = {The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer},
      journal = {Journal of Electrical and Electronic Engineering},
      volume = {4},
      number = {2},
      pages = {31-34},
      doi = {10.11648/j.jeee.20160402.15},
      url = {https://doi.org/10.11648/j.jeee.20160402.15},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20160402.15},
      abstract = {In this paper, to build a predictive model of hepatitis B virus (HBV) reactivation in primary liver cancer (PLC) patients after precise radiotherapy (RT). Logistic regression analysis was adopted to extract the optimal feature subset, TNM, HBV DNA level and outer margin of RT were risk factors for HBV reactivation (P < 0.05). A predictive model of support vector machine (SVM) was established for the optimal feature subset and all of PLC data sets. The experimental results proved that the former obviously improves the classification accuracy, which increased from 74.44% to 78.89%. In this paper, it is concluded that TNM, HBV DNA levels and outer boundary are the risk factor for HBV reactivation (P < 0.05).},
     year = {2016}
    }
    

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  • TY  - JOUR
    T1  - The Predictive Model of Hepatitis B Virus Reactivation Induced by Precise Radiotherapy in Primary Liver Cancer
    AU  - Wang Shuai
    AU  - Wu Guan-peng
    AU  - Huang Wei
    AU  - Liu Tong-hai
    AU  - Yin Yong
    AU  - Liu Yi-hui
    Y1  - 2016/04/07
    PY  - 2016
    N1  - https://doi.org/10.11648/j.jeee.20160402.15
    DO  - 10.11648/j.jeee.20160402.15
    T2  - Journal of Electrical and Electronic Engineering
    JF  - Journal of Electrical and Electronic Engineering
    JO  - Journal of Electrical and Electronic Engineering
    SP  - 31
    EP  - 34
    PB  - Science Publishing Group
    SN  - 2329-1605
    UR  - https://doi.org/10.11648/j.jeee.20160402.15
    AB  - In this paper, to build a predictive model of hepatitis B virus (HBV) reactivation in primary liver cancer (PLC) patients after precise radiotherapy (RT). Logistic regression analysis was adopted to extract the optimal feature subset, TNM, HBV DNA level and outer margin of RT were risk factors for HBV reactivation (P < 0.05). A predictive model of support vector machine (SVM) was established for the optimal feature subset and all of PLC data sets. The experimental results proved that the former obviously improves the classification accuracy, which increased from 74.44% to 78.89%. In this paper, it is concluded that TNM, HBV DNA levels and outer boundary are the risk factor for HBV reactivation (P < 0.05).
    VL  - 4
    IS  - 2
    ER  - 

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Author Information
  • School of Information, Qilu University of Technology, Jinan, China

  • School of Information, Qilu University of Technology, Jinan, China

  • Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China

  • Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China

  • Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China

  • School of Information, Qilu University of Technology, Jinan, China

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