title: Prediction of radiation induced liver disease using artificial neural networks.
abstract: 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.
authors.affiliation: Department of Radiation Oncology, Fudan University Cancer Hospital, Shanghai 200032, China.
authors.forename: Ji
authors.initials: J
authors.lastname: Zhu
authors.forename: Xiao-Dong
authors.initials: XD
authors.lastname: Zhu
authors.forename: Shi-Xiong
authors.initials: SX
authors.lastname: Liang
authors.forename: Zi-Yong
authors.initials: ZY
authors.lastname: Xu
authors.forename: Jian-Dong
authors.initials: JD
authors.lastname: Zhao
authors.forename: Qi-Fang
authors.initials: QF
authors.lastname: Huang
authors.forename: An-Yu
authors.initials: AY
authors.lastname: Wang
authors.forename: Long
authors.initials: L
authors.lastname: Chen
authors.forename: Xiao-Long
authors.initials: XL
authors.lastname: Fu
authors.forename: Guo-Liang
authors.initials: GL
authors.lastname: Jiang
mesh_terms: 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