title: Malignant and benign breast masses on 3D US volumetric images: effect of computer-aided diagnosis on radiologist accuracy.
abstract: OBJECTIVE
  To retrospectively investigate the effect of using a custom-designed computer classifier on radiologists' sensitivity and specificity for discriminating malignant masses from benign masses on three-dimensional (3D) volumetric ultrasonographic (US) images, with histologic analysis serving as the reference standard.
  
  
  METHODS
  Informed consent and institutional review board approval were obtained. Our data set contained 3D US volumetric images obtained in 101 women (average age, 51 years; age range, 25-86 years) with 101 biopsy-proved breast masses (45 benign, 56 malignant). A computer algorithm was designed to automatically delineate mass boundaries and extract features on the basis of segmented mass shapes and margins. A computer classifier was used to merge features into a malignancy score. Five experienced radiologists participated as readers. Each radiologist read cases first without computer-aided diagnosis (CAD) and immediately thereafter with CAD. Observers' malignancy rating data were analyzed with the receiver operating characteristic (ROC) curve.
  
  
  RESULTS
  Without CAD, the five radiologists had an average area under the ROC curve (A(z)) of 0.83 (range, 0.81-0.87). With CAD, the average A(z) increased significantly (P = .006) to 0.90 (range, 0.86-0.93). When a 2% likelihood of malignancy was used as the threshold for biopsy recommendation, the average sensitivity of radiologists increased from 96% to 98% with CAD, while the average specificity for this data set decreased from 22% to 19%. If a biopsy recommendation threshold could be chosen such that sensitivity would be maintained at 96%, specificity would increase to 45% with CAD.
  
  
  CONCLUSIONS
  Use of a computer algorithm may improve radiologists' accuracy in distinguishing malignant from benign breast masses on 3D US volumetric images.
authors.affiliation: Department of Radiology, University of Michigan Medical Center, CGC B2102, 1500 E Medical Center Dr, Ann Arbor, MI 48109-0904, USA. berki@umich.edu
authors.forename: Berkman
authors.initials: B
authors.lastname: Sahiner
authors.forename: Heang-Ping
authors.initials: HP
authors.lastname: Chan
authors.forename: Marilyn A
authors.initials: MA
authors.lastname: Roubidoux
authors.forename: Lubomir M
authors.initials: LM
authors.lastname: Hadjiiski
authors.forename: Mark A
authors.initials: MA
authors.lastname: Helvie
authors.forename: Chintana
authors.initials: C
authors.lastname: Paramagul
authors.forename: Janet
authors.initials: J
authors.lastname: Bailey
authors.forename: Alexis V
authors.initials: AV
authors.lastname: Nees
authors.forename: Caroline
authors.initials: C
authors.lastname: Blane
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008875:Middle Aged; D015588:Observer Variation; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013647:Task Performance and Analysis; D016217:Ultrasonography, Mammary / Q000379:methods*