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

Wang Shuai; Wu Guan-peng; Huang Wei; Liu Tong-hai; Yin Yong; Liu Yi-hui

doi:doi:10.11648/j.jeee.20160402.15

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

Wang Shuai, Wu Guan-peng, Huang Wei, Liu Tong-hai, Yin Yong, Liu Yi-hui

Published in Journal of Electrical and Electronic Engineering (Volume 4, Issue 2)

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

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[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.
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[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.
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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

Wang Shuai

School of Information, Qilu University of Technology, Jinan, China
Wu Guan-peng

School of Information, Qilu University of Technology, Jinan, China
Huang Wei

Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
Liu Tong-hai

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

Department of Radiation Oncology, Shandong Cancer Hospital, Shandong Academy of Medical Sciences, Jinan, China
Liu Yi-hui

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

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Plain Text BibTeX RIS

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

Copy | Download

@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}
}

Copy | Download

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  -

Copy | Download