title: Computer-aided detection of breast masses: four-view strategy for screening mammography.
abstract: OBJECTIVE
  To improve the performance of a computer-aided detection (CAD) system for mass detection by using four-view information in screening mammography.
  
  
  METHODS
  The authors developed a four-view CAD system that emulates radiologists' reading by using the craniocaudal and mediolateral oblique views of the ipsilateral breast to reduce false positives (FPs) and the corresponding views of the contralateral breast to detect asymmetry. The CAD system consists of four major components: (1) Initial detection of breast masses on individual views, (2) information fusion of the ipsilateral views of the breast (referred to as two-view analysis), (3) information fusion of the corresponding views of the contralateral breast (referred to as bilateral analysis), and (4) fusion of the four-view information with a decision tree. The authors collected two data sets for training and testing of the CAD system: A mass set containing 389 patients with 389 biopsy-proven masses and a normal set containing 200 normal subjects. All cases had four-view mammograms. The true locations of the masses on the mammograms were identified by an experienced MQSA radiologist. The authors randomly divided the mass set into two independent sets for cross validation training and testing. The overall test performance was assessed by averaging the free response receiver operating characteristic (FROC) curves of the two test subsets. The FP rates during the FROC analysis were estimated by using the normal set only. The jackknife free-response ROC (JAFROC) method was used to estimate the statistical significance of the difference between the test FROC curves obtained with the single-view and the four-view CAD systems.
  
  
  RESULTS
  Using the single-view CAD system, the breast-based test sensitivities were 58% and 77% at the FP rates of 0.5 and 1.0 per image, respectively. With the four-view CAD system, the breast-based test sensitivities were improved to 76% and 87% at the corresponding FP rates, respectively. The improvement was found to be statistically significant (p < 0.0001) by JAFROC analysis.
  
  
  CONCLUSIONS
  The four-view information fusion approach that emulates radiologists' reading strategy significantly improves the performance of breast mass detection of the CAD system in comparison with the single-view approach.
authors.affiliation: Department of Radiology, University of Michigan, 1500 East Medical Center Drive, C478 Med-Inn Building, Ann Arbor, Michigan 48109-5842, USA. jvwei@med.umich.edu
authors.forename: Jun
authors.initials: J
authors.lastname: Wei
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Chuan
authors.initials: C
authors.lastname: Zhou
authors.forename: Yi-Ta
authors.initials: YT
authors.lastname: Wu
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Lubomir M
authors.initials: LM
authors.lastname: Hadjiiski
authors.forename: Marilyn A
authors.initials: MA
authors.lastname: Roubidoux
authors.forename: Mark A
authors.initials: MA
authors.lastname: Helvie
mesh_terms: D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D006801:Humans; D008327:Mammography / Q000379:methods*; D012372:ROC Curve; D012189:Retrospective Studies; D012680:Sensitivity and Specificity