title: Breast cancer risk estimation with artificial neural networks revisited: discrimination and calibration.
abstract: BACKGROUND
  Discriminating malignant breast lesions from benign ones and accurately predicting the risk of breast cancer for individual patients are crucial to successful clinical decisions. In the past, several artificial neural network (ANN) models have been developed for breast cancer-risk prediction. All studies have reported discrimination performance, but not one has assessed calibration, which is an equivalently important measure for accurate risk prediction. In this study, the authors have evaluated whether an artificial neural network (ANN) trained on a large prospectively collected dataset of consecutive mammography findings can discriminate between benign and malignant disease and accurately predict the probability of breast cancer for individual patients.
  
  
  METHODS
  Our dataset consisted of 62,219 consecutively collected mammography findings matched with the Wisconsin State Cancer Reporting System. The authors built a 3-layer feedforward ANN with 1000 hidden-layer nodes. The authors trained and tested their ANN by using 10-fold cross-validation to predict the risk of breast cancer. The authors used area the under the receiver-operating characteristic curve (AUC), sensitivity, and specificity to evaluate discriminative performance of the radiologists and their ANN. The authors assessed the accuracy of risk prediction (ie, calibration) of their ANN by using the Hosmer-Lemeshow (H-L) goodness-of-fit test.
  
  
  RESULTS
  Their ANN demonstrated superior discrimination (AUC, 0.965) compared with the radiologists (AUC, 0.939; P<.001). The authors' ANN was also well calibrated as shown by an H-L goodness of fit P-value of .13.
  
  
  CONCLUSIONS
  The authors' ANN can effectively discriminate malignant abnormalities from benign ones and accurately predict the risk of breast cancer for individual abnormalities.
authors.affiliation: Industrial and Systems Engineering Department, University of Wisconsin, Madison, Wisconsin 53792-3252, USA.
authors.forename: Turgay
authors.initials: T
authors.lastname: Ayer
authors.forename: Oguzhan
authors.initials: O
authors.lastname: Alagoz
authors.forename: Jagpreet
authors.initials: J
authors.lastname: Chhatwal
authors.forename: Jude W
authors.initials: JW
authors.lastname: Shavlik
authors.forename: Charles E
authors.initials: CE
authors.lastname: Kahn
authors.forename: Elizabeth S
authors.initials: ES
authors.lastname: Burnside
mesh_terms: D000368:Aged; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002138:Calibration; D003658:Decision Making, Computer-Assisted; D003936:Diagnosis, Computer-Assisted; D004192:Discrimination, Psychological; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008327:Mammography; D008875:Middle Aged; D016571:Neural Networks, Computer; D018570:Risk Assessment; D012680:Sensitivity and Specificity