title: Predicting breast cancer invasion with artificial neural networks on the basis of mammographic features.
abstract: OBJECTIVE
  To evaluate whether an artificial neural network (ANN) can predict breast cancer invasion on the basis of readily available medical findings (ie, mammographic findings classified according to the American College of Radiology Breast Imaging Reporting and Data System and patient age).
  
  
  METHODS
  In 254 adult patients, 266 lesions that had been sampled at biopsy were randomly selected for the study. There were 96 malignant and 170 benign lesions. On the basis of nine mammographic findings and patient age, a three-layer backpropagation network was developed to predict whether the malignant lesions were in situ or invasive.
  
  
  RESULTS
  The ANN predicted invasion among malignant lesions with an area under the receiver operating characteristic curve (Az) of .91 +/- .03. It correctly identified all 28 in situ cancers (specificity, 100%) and 48 of 68 invasive cancers (sensitivity, 71%).
  
  
  CONCLUSIONS
  The ANN used mammographic features and patient age to accurately classify invasion among breast cancers, information that was previously available only by means of biopsy. This knowledge may assist in surgical planning and may help reduce the cost and morbidity of unnecessary biopsy.
authors.affiliation: Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
authors.forename: J Y
authors.initials: JY
authors.lastname: Lo
authors.forename: J A
authors.initials: JA
authors.lastname: Baker
authors.forename: P J
authors.initials: PJ
authors.lastname: Kornguth
authors.forename: J D
authors.initials: JD
authors.lastname: Iglehart
authors.forename: C E
authors.initials: CE
authors.lastname: Floyd
mesh_terms: D001706:Biopsy; D001943:Breast Neoplasms / Q000000981:diagnostic imaging / Q000473:pathology*; D005260:Female; D006801:Humans; D008327:Mammography; D009361:Neoplasm Invasiveness; D016571:Neural Networks, Computer; D012372:ROC Curve; D011857:Radiographic Image Interpretation, Computer-Assisted; D012189:Retrospective Studies; D012680:Sensitivity and Specificity