title: Improving breast cancer diagnosis with computer-aided diagnosis.
abstract: OBJECTIVE
  The purpose of this study was to test whether computer-aided diagnosis (CAD) can improve radiologists' performance in breast cancer diagnosis.
  
  
  METHODS
  The computer classification scheme used in this study estimates the likelihood of malignancy for clustered microcalcifications based on eight computer-extracted features obtained from standard-view mammograms. One hundred four histologically verified cases of microcalcifications (46 malignant, 58 benign) in a near-consecutive biopsy series were used in this study. Observer performance was measured on 10 radiologists who read the original standard- and magnification-view mammograms. The computer aid provided a percentage estimate of the likelihood of malignancy. Comparison was made between computer-aided performance and unaided (routine clinical) performance by using receiver operating characteristic (ROC) analysis and by comparing biopsy recommendations.
  
  
  RESULTS
  The average ROC curve area (Az) increased from 0.61 without aid to 0.75 with the computer aid (P < .0001). On average, with the computer aid, each observer recommended 6.4 additional biopsies for cases with malignant lesions (P = .0006) and 6.0 fewer biopsies for cases with benign lesions (P = .003). This improvement corresponded to increases in sensitivity (from 73.5% to 87.4%), specificity (from 31.6% to 41.9%), and hypothetical positive biopsy yield (from 46% to 55%).
  
  
  CONCLUSIONS
  CAD can be used to improve radiologists' performance in breast cancer diagnosis.
authors.affiliation: Kurt Rossmann Laboratories for Radiologic Image Research, Department of Radiology, University of Chicago, IL 60637, USA.
authors.forename: Y
authors.initials: Y
authors.lastname: Jiang
authors.forename: R M
authors.initials: RM
authors.lastname: Nishikawa
authors.forename: R A
authors.initials: RA
authors.lastname: Schmidt
authors.forename: C E
authors.initials: CE
authors.lastname: Metz
authors.forename: M L
authors.initials: ML
authors.lastname: Giger
authors.forename: K
authors.initials: K
authors.lastname: Doi
mesh_terms: D019540:Area Under Curve; D001706:Biopsy; D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging* / Q000473:pathology; D002114:Calcinosis / Q000000981:diagnostic imaging / Q000473:pathology; D003936:Diagnosis, Computer-Assisted; D005260:Female; D018226:Fibroadenoma / Q000000981:diagnostic imaging / Q000473:pathology; D005348:Fibrocystic Breast Disease / Q000000981:diagnostic imaging / Q000473:pathology; D005500:Follow-Up Studies; D006801:Humans; D016013:Likelihood Functions; D008327:Mammography / Q000145:classification; D016571:Neural Networks, Computer; D015588:Observer Variation; D012372:ROC Curve; D011871:Radiology; D012680:Sensitivity and Specificity; D013997:Time Factors