Prediction of Radiation Induced Liver Disease Using Artificial Neural Networks

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 cirrho...

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Bibliographic Details
Published in:Japanese journal of clinical oncology 2006-12, Vol.36 (12), p.783-788
Main Authors: Zhu, Ji, Zhu, Xiao-Dong, Liang, Shi-Xiong, Xu, Zi-Yong, Zhao, Jian-Dong, Huang, Qi-Fang, Wang, An-Yu, Chen, Long, Fu, Xiao-Long, Jiang, Guo-Liang
Format: Article
Language:English
Subjects:
Algorithms
artificial neural networks
Female
Humans
Liver Diseases - etiology
Liver Diseases - radiotherapy
Male
Middle Aged
Neural Networks (Computer)
Nuclear polyhedrosis virus
primary liver carcinoma
Prognosis
radiation induced liver disease
Radiation Injuries - etiology
Radiotherapy Planning, Computer-Assisted
Radiotherapy, Conformal - adverse effects
Sensitivity and Specificity
three-dimensional conformal radiation therapy
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Summary: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.
ISSN:0368-2811
1465-3621
DOI:10.1093/jjco/hyl117