Skip Navigation



Japanese Journal of Clinical Oncology Advance Access published online on October 26, 2006
Japanese Journal of Clinical Oncology, doi:10.1093/jjco/hyl117
This Article
Right arrow FREE Full Text (PDF) Freely available
Right arrow All Versions of this Article:
36/12/783    most recent
hyl117v1
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Similar articles in PubMed
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrow Request Permissions
Google Scholar
Right arrow Articles by Zhu, J.
Right arrow Articles by Jiang, G.-L.
Right arrow Search for Related Content
PubMed
Right arrow PubMed Citation
Right arrow Articles by Zhu, J.
Right arrow Articles by Jiang, G.-L.
Social Bookmarking
Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

© 2006 Foundation for Promotion of Cancer Research
Received June 7, 2006
Accepted August 10, 2006

Original Article

Prediction of Radiation Induced Liver Disease Using Artificial Neural Networks

Ji Zhu 1, Xiao-Dong Zhu 2, Shi-Xiong Liang 2, Zi-Yong Xu 3, Jian-Dong Zhao 1, Qi-Fang Huang 2, An-Yu Wang 2, Long Chen 2, Xiao-Long Fu 1, and Guo-Liang Jiang 4 *

1 Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai, china; Department of Oncology, Shanghai Medical college, Fudan University, Shanghai, china
2 Department of Radiation Oncology, Cancer Hospital, Guangxi Medical University, Nanning
3 Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai
4 Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai, china; Department of Oncology, Shanghai Medical college, Fudan University, 270 Dong An Road, Shanghai 200032, China

* To whom correspondence should be addressed.
Guo-Liang Jiang, E-mail: jianggl{at}21cn.com


  Abstract

Objective To evaluate the efficiency of predicting radiation induced liver disease (RILD) with an artificial neural network (ANN) model.

Methods and Materials From August 2000 to November 2004, a total of 93 primary liver carcinoma (PLC) patients with single lesion and associated with hepatic cirrhosis of Child-Pugh grade A, were treated with hypofractionated three-dimensional conformal radiotherapy (3DCRT). Eight out of 93 patients were diagnosed RILD. Ninety-three patients were randomly divided into two subsets (training set and verification set). In model A, the ratio of patient numbers was 1:1 for training and verification set, and in model B, the ratio was 2:1.

Results The areas under receiver-operating characteristic (ROC) curves were 0.8897 and 0.8831 for model A and B, respectively. Sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV) were 0.875 (7/8), 0.882 (75/85), 0.882 (82/93), 0.412 (7/17) and 0.987 (75/76) for model A, and 0.750 (6/8), 0.800 (68/85), 0.796 (74/93), 0.261 (6/23) and 0.971 (68/70) for model B.

Conclusion ANN was proved high accuracy for prediction of RILD. It could be used together with other models and dosimetric parameters to evaluate hepatic irradiation plans.

Keywords: primary liver carcinoma; three-dimensional conformal radiation therapy; radiation induced liver disease; artificial neural networks.
Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?




Disclaimer: Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.