title: Comparison of conventional and automated breast volume ultrasound in the description and characterization of solid breast masses based on BI-RADS features.
abstract: OBJECTIVE
  To compare the performance of radiologists in the use of conventional ultrasound (US) and automated breast volume ultrasound (ABVU) for the characterization of benign and malignant solid breast masses based on breast imaging and reporting data system (BI-RADS) criteria.
  
  
  METHODS
  Conventional US and ABVU images were obtained in 87 patients with 106 solid breast masses (52 cancers, 54 benign lesions). Three experienced radiologists who were blinded to all examination results independently characterized the lesions and reported a BI-RADS assessment category and a level of suspicion of malignancy. The results were analyzed by calculation of Cohen's κ coefficient and by receiver operating characteristic (ROC) analysis.
  
  
  RESULTS
  Assessment of the agreement of conventional US and ABVU indicated that the posterior echo feature was the most discordant feature of seven features (κ = 0.371 ± 0.225) and that orientation had the greatest agreement (κ = 0.608 ± 0.210). The final assessment showed substantial agreement (κ = 0.773 ± 0.104). The areas under the ROC curves (Az) for conventional US and ABVU were not statistically significant for each reader, but the mean Az values of conventional US and ABVU by multi-reader multi-case analysis were significantly different (conventional US 0.991, ABVU 0.963; 95 % CI -0.0471 to -0.0097). The means for sensitivity, specificity, positive predictive value, and negative predictive value of conventional US and ABVU did not differ significantly.
  
  
  CONCLUSIONS
  There was substantial inter-observer agreement in the final assessment of solid breast masses by conventional US and ABVU. ROC analysis comparing the performance of conventional US and ABVU indicated a marginally significant difference in mean Az, but not in mean sensitivity, specificity, positive predictive value, or negative predictive value.
authors.affiliation: Department of Radiology, Research Institute of Radiology, Asan Medical Center, College of Medicine, University of Ulsan, 388-1 Pungnap-2dong, Songpa-Gu, Seoul, 138-736, Korea.
authors.forename: Hyunji
authors.initials: H
authors.lastname: Kim
authors.forename: Joo Hee
authors.initials: JH
authors.lastname: Cha
authors.forename: Ha-Yeun
authors.initials: HY
authors.lastname: Oh
authors.forename: Hak Hee
authors.initials: HH
authors.lastname: Kim
authors.forename: Hee Jung
authors.initials: HJ
authors.lastname: Shin
authors.forename: Eun Young
authors.initials: EY
authors.lastname: Chae
mesh_terms: D001331:Automation; 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; D018275:Carcinoma, Lobular / Q000000981:diagnostic imaging* / Q000473:pathology; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D009361:Neoplasm Invasiveness; D009367:Neoplasm Staging; D015588:Observer Variation; D011237:Predictive Value of Tests; D012372:ROC Curve; D011873:Radiology Information Systems; D016217:Ultrasonography, Mammary