title: Differentiation of benign and malignant breast lesions: a comparison between automatically generated breast volume scans and handheld ultrasound examinations.
abstract: OBJECTIVE
  To assess the diagnostic value of automated breast volume scanning (ABVS) or conventional handheld ultrasonography (HHUS) for the differentiation of benign and malignant breast lesions.
  
  
  METHODS
  The study prospectively evaluated 239 lesions in 213 women who were scheduled for open biopsy. The patients underwent ABVS and conventional HHUS. The sensitivity, specificity, accuracy, false positive rate, false negative rate, and positive and negative predictive values for HHUS and ABVS images were calculated using histopathological examination as the gold standard. Additionally, diagnostic accuracy was further evaluated according to the size of the masses.
  
  
  RESULTS
  Among the 239 breast lesions studied, pathology revealed 85 (35.6%) malignant lesions and 154 (64.4%) benign lesions. ABVS was similar to HHUS in terms of sensitivity (95.3% vs. 90.6%), specificity (80.5% vs. 82.5%), accuracy (85.8% vs. 85.3%), positive predictive value (73.0% vs. 74.0%), and negative predictive value (93.3% vs. 94.1%). The area under the receiver operating characteristic (ROC) curve, which is used to estimate the accuracy of the methods, demonstrated only minor differences between HHUS and ABVS (0.928 and 0.948, respectively).
  
  
  CONCLUSIONS
  The diagnostic accuracy of HHUS and ABVS in differentiating benign from malignant breast lesions is almost identical. However, ABVS can offer new diagnostic information. ABVS may help to distinguish between real lesions and inhomogeneous areas, find small lesions, and demonstrate the presence of intraductal lesions. This technique is feasible for clinical applications and is a promising new technique in breast imaging.
authors.affiliation: Department of Diagnostic Ultrasound, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, China.
authors.forename: Hong-Yan
authors.initials: HY
authors.lastname: Wang
authors.forename: Yu-Xin
authors.initials: YX
authors.lastname: Jiang
authors.forename: Qing-Li
authors.initials: QL
authors.lastname: Zhu
authors.forename: Jing
authors.initials: J
authors.lastname: Zhang
authors.forename: Qing
authors.initials: Q
authors.lastname: Dai
authors.forename: He
authors.initials: H
authors.lastname: Liu
authors.forename: Xing-Jian
authors.initials: XJ
authors.lastname: Lai
authors.forename: Qiang
authors.initials: Q
authors.lastname: Sun
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002648:Child; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008875:Middle Aged; D008904:Miniaturization; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016217:Ultrasonography, Mammary / Q000295:instrumentation / Q000379:methods*; D055815:Young Adult