title: Comparison of automated breast volume scanning and hand- held ultrasound in the detection of breast cancer: an analysis of 5,566 patient evaluations.
abstract: BACKGROUND
  The purpose of this study was to compare the accuracy and effectiveness of automated breast volume scanning (ABVS) and hand-held ultrasound (HHUS) in the detection of breast cancer in a large population group with a long-term follow-up, and to investigate whether different ultrasound systems may influence the estimation of cancer detection.
  
  
  METHODS
  Institutional review board approval was obtained for this retrospective study, and informed consent was waived. From September 2010 to August 2011, a total of 1,866 ABVS and 3,700 HHUS participants, who underwent these procedures at our institute, were included in this study. Cancers occurring during the study and subsequent follow-up were evaluated. The reference standard was a combination of histology and follow-up imaging (≥12 months). The recall rate, cancer detection yield, diagnostic accuracy, sensitivity, specificity, and positive (PPV) and negative (NPV) predictive values were calculated with exact 95% confidence intervals.
  
  
  RESULTS
  The recall rate was 2.57 per 1,000 (48/1,866) for ABVS and 3.57 per 1,000 (132/3,700) for HHUS, with ? significant difference (p=0.048). The cancer detection yield was 3.8 per 1,000 for ABVS and 2.7 per 1,000 for HHUS. The diagnostic accuracy was 97.7% for ABVS and 96.5% for HHUS with statistical significance (p=0.018). The specificity of ABVS and HHUS were 97.8%, 96.7%, respectively (p=0.022).
  
  
  CONCLUSIONS
  ABVS shows a comparable diagnostic performance to HHUS. ABVS is an effective supplemental tool for mammography in breast cancer detection in a large population.
authors.affiliation: Department of Radiology, Research Institute of Radiology, Asan Medical Center, College of Medicine, University of Ulsan, Seoul, Korea E-mail : jhcha@amc.seoul.kr.
authors.forename: Woo Jung
authors.initials: WJ
authors.lastname: Choi
authors.forename: Joo Hee
authors.initials: JH
authors.lastname: Cha
authors.forename: Hak Hee
authors.initials: HH
authors.lastname: Kim
authors.forename: Hee Jung
authors.initials: HJ
authors.lastname: Shin
authors.forename: Hyunji
authors.initials: H
authors.lastname: Kim
authors.forename: Eun Young
authors.initials: EY
authors.lastname: Chae
authors.forename: Min Ji
authors.initials: MJ
authors.lastname: Hong
mesh_terms: D002288:Adenocarcinoma, Mucinous / Q000000981:diagnostic imaging* / Q000473:pathology; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001940:Breast / Q000473:pathology*; D001943:Breast Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology; D018270:Carcinoma, Ductal, Breast / Q000000981:diagnostic imaging* / Q000473:pathology; D002285:Carcinoma, Intraductal, Noninfiltrating / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008875:Middle Aged; D008904:Miniaturization; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D010363:Pattern Recognition, Automated; D011379:Prognosis; D012189:Retrospective Studies; D012680:Sensitivity and Specificity; D047368:Tumor Burden; D016217:Ultrasonography, Mammary / Q000295:instrumentation / Q000379:methods*; D055815:Young Adult