title: Performance evaluation of radiologists with artificial neural network for differential diagnosis of intra-axial cerebral tumors on MR images.
abstract: OBJECTIVE
  Previous studies have suggested that use of an artificial neural network (ANN) system is beneficial for radiological diagnosis. Our purposes in this study were to construct an ANN for the differential diagnosis of intra-axial cerebral tumors on MR images and to evaluate the effect of ANN outputs on radiologists' diagnostic performance.
  
  
  METHODS
  We collected MR images of 126 patients with intra-axial cerebral tumors (58 high-grade gliomas, 37 low-grade gliomas, 19 metastatic tumors, and 12 malignant lymphomas). We constructed a single 3-layer feed-forward ANN with a Levenberg-Marquardt algorithm. The ANN was designed to differentiate among 4 categories of tumors (high-grade gliomas, low-grade gliomas, metastases, and malignant lymphomas) with use of 2 clinical parameters and 13 radiologic findings in MR images. Subjective ratings for the 13 radiologic findings were provided independently by 2 attending radiologists. All 126 cases were used for training and testing of the ANN based on a leave-one-out-by-case method. In the observer test, MR images were viewed by 9 radiologists, first without and then with ANN outputs. Each radiologist's performance was evaluated through a receiver operating characteristic (ROC) analysis on a continuous rating scale.
  
  
  RESULTS
  The averaged area under the ROC curve for ANN alone was 0.949. The diagnostic performance of the 9 radiologists increased from 0.899 to 0.946 (P < .001) when they used ANN outputs.
  
  
  CONCLUSIONS
  The ANN can provide useful output as a second opinion to improve radiologists' diagnostic performance in the differential diagnosis of intra-axial cerebral tumors seen on MR imaging.
authors.affiliation: Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
authors.forename: K
authors.initials: K
authors.lastname: Yamashita
authors.forename: T
authors.initials: T
authors.lastname: Yoshiura
authors.forename: H
authors.initials: H
authors.lastname: Arimura
authors.forename: F
authors.initials: F
authors.lastname: Mihara
authors.forename: T
authors.initials: T
authors.lastname: Noguchi
authors.forename: A
authors.initials: A
authors.lastname: Hiwatashi
authors.forename: O
authors.initials: O
authors.lastname: Togao
authors.forename: Y
authors.initials: Y
authors.lastname: Yamashita
authors.forename: T
authors.initials: T
authors.lastname: Shono
authors.forename: S
authors.initials: S
authors.lastname: Kumazawa
authors.forename: Y
authors.initials: Y
authors.lastname: Higashida
authors.forename: H
authors.initials: H
authors.lastname: Honda
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001932:Brain Neoplasms / Q000175:diagnosis*; D002648:Child; D002675:Child, Preschool; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D007223:Infant; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity