title: Usefulness of artificial neural network for differential diagnosis of hepatic masses on CT images.
abstract: OBJECTIVE
  Our purpose in this study is to apply an artificial neural network (ANN) for differential diagnosis of certain hepatic masses on computed tomographic (CT) images and evaluate the effect of ANN output on radiologist diagnostic performance.
  
  
  METHODS
  We collected 120 cases of hepatic disease. We used a single three-layer feed-forward ANN with a back-propagation algorithm. The ANN is designed to differentiate four hepatic masses (hepatocellular carcinoma, intrahepatic peripheral cholangiocarcinoma, hemangioma, and metastasis) by using nine clinical parameters and 24 radiological findings in dual-phase contrast-enhanced CT images. Thus, the ANN consisted of 33 input units and four output units. Subjective ratings for the 24 radiological findings were provided independently by two attending radiologists. All clinical cases were used for training and testing of the ANN by implementation of a round-robin technique. In the observer test, CT images of all 120 cases (30 cases for each disease) were used. CT images were viewed by seven radiologists first without and then with ANN output. Radiologist performance was evaluated by using receiver operating characteristic (ROC) analysis on a continuous rating scale.
  
  
  RESULTS
  Averaged area under the ROC curve for ANN alone was 0.961. The diagnostic performance of seven radiologists increased from 0.888 to 0.934 (P < .02) when they used ANN output.
  
  
  CONCLUSIONS
  The ANN can provide useful output as a second opinion to improve radiologist diagnostic performance in the differential diagnosis of hepatic masses seen on contrast-enhanced CT.
authors.affiliation: Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, 3-1-1, Maidashi Fukuoka, 812-8582, Japan. matake@fukuoka-med.jrc.or.jp
authors.forename: Kunishige
authors.initials: K
authors.lastname: Matake
authors.forename: Kengo
authors.initials: K
authors.lastname: Yoshimitsu
authors.forename: Seiji
authors.initials: S
authors.lastname: Kumazawa
authors.forename: Yoshiharu
authors.initials: Y
authors.lastname: Higashida
authors.forename: Hiroyuki
authors.initials: H
authors.lastname: Irie
authors.forename: Yoshiki
authors.initials: Y
authors.lastname: Asayama
authors.forename: Tomohiro
authors.initials: T
authors.lastname: Nakayama
authors.forename: Daisuke
authors.initials: D
authors.lastname: Kakihara
authors.forename: Shigehiko
authors.initials: S
authors.lastname: Katsuragawa
authors.forename: Kunio
authors.initials: K
authors.lastname: Doi
authors.forename: Hiroshi
authors.initials: H
authors.lastname: Honda
mesh_terms: D000369:Aged, 80 and over; D000465:Algorithms; D002983:Clinical Competence; D003287:Contrast Media; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007479:Iopamidol; D008113:Liver Neoplasms / Q000000981:diagnostic imaging*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D015588:Observer Variation; D012372:ROC Curve; D014057:Tomography, X-Ray Computed
chemical_list: D003287:Contrast Media; D007479:Iopamidol