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
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.


CONCLUSIONS
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.
Zhu
Ji
J
Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai 200032, China.
Zhu
Xiao-Dong
XD
Liang
Shi-Xiong
SX
Xu
Zi-Yong
ZY
Zhao
Jian-Dong
JD
Huang
Qi-Fang
QF
Wang
An-Yu
AY
Chen
Long
L
Fu
Xiao-Long
XL
Jiang
Guo-Liang
GL
D000465:Algorithms; D005260:Female; D006801:Humans; D008107:Liver Diseases / Q000209:etiology* / Q000532:radiotherapy; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D011379:Prognosis; D011832:Radiation Injuries / Q000209:etiology*; D011880:Radiotherapy Planning, Computer-Assisted; D020266:Radiotherapy, Conformal / Q000009:adverse effects*; D012680:Sensitivity and Specificity