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Japanese Journal of Clinical Oncology Advance Access originally published online on October 26, 2006
Japanese Journal of Clinical Oncology 2006 36(12):783-788; doi:10.1093/jjco/hyl117
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© 2006 Foundation for Promotion of Cancer Research

Prediction of Radiation Induced Liver Disease Using Artificial Neural Networks

Ji Zhu1,3,*, Xiao-Dong Zhu2,*, Shi-Xiong Liang2, Zi-Yong Xu1, Jian-Dong Zhao1,3, Qi-Fang Huang2, An-Yu Wang2, Long Chen2, Xiao-Long Fu1,3 and Guo-Liang Jiang1,3,

1 Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai
2 Department of Radiation Oncology, Cancer Hospital, Guangxi Medical University, Nanning
3 Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, China

For reprints and all correspondence: Guo-Liang Jiang, Department of Radiation Oncology, Fudan University Cancer Hospital, 270 Dong An Road, Shanghai 200032, China. E-mail: jianggl{at}21cn.com

Received June 7, 2006; accepted August 10, 2006

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.

Key Words: primary liver carcinoma • three-dimensional conformal radiation therapy • radiation induced liver disease • artificial neural networks

* Ji Zhu and Xiao-Dong Zhu contributed equally to the current study.


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