title: Bayesian probability of malignancy with BI-RADS sonographic features.
abstract: OBJECTIVE
  The purpose of this study was to develop a quantitative approach for combining individual American College of Radiology Breast Imaging Reporting and Data System (BI-RADS) sonographic features of breast masses for assessing the overall probability of malignancy.
  
  
  METHODS
  Sonograms of solid breast masses were analyzed by 2 observers blinded to patient age, mammographic features, and lesion pathologic findings. BI-RADS sonographic features were determined by using American College of Radiology criteria. A naïve Bayes model was used to determine the probability of malignancy of all the sonographic features together and with age and BI-RADS mammographic features. The diagnostic performance for various combinations was evaluated by using the area under the receiver operating curve (Az).
  
  
  RESULTS
  Sonographic features had high positive and negative predictive values. The Az values for BI-RADS sonographic features for the 2 observers ranged from 0.772 to 0.884, which increased to 0.866 to 0.924 when used with patient age and BI-RADS mammographic features. The benefit of adding age and mammographic information was more marked for the observer with lower initial diagnostic performance. Age-specific analysis showed that diagnostic performance varied with age, with higher performance for patients aged 45 years and younger and patients older than 60 years compared to those aged 46 to 60 years. In 85% of cases, the diagnosis of the observers matched. When the consensus between the observers was used for diagnostic decisions, a high level of diagnostic performance (Az, 0.954) was achieved.
  
  
  CONCLUSIONS
  A naïve Bayes model provides a systematic approach for combining sonographic features and other patient characteristics for assessing the probability of malignancy to differentiate malignant and benign breast masses.
authors.affiliation: Department of Radiology, University of Pennsylvania, 1 Silverstein, 3400 Spruce St, Philadelphia, PA 19104 USA. chandra.sehgal@uphs.upenn.edu.
authors.forename: Ghizlane
authors.initials: G
authors.lastname: Bouzghar
authors.forename: Benjamin J
authors.initials: BJ
authors.lastname: Levenback
authors.forename: Laith R
authors.initials: LR
authors.lastname: Sultan
authors.forename: Santosh S
authors.initials: SS
authors.lastname: Venkatesh
authors.forename: Alyssa
authors.initials: A
authors.lastname: Cwanger
authors.forename: Emily F
authors.initials: EF
authors.lastname: Conant
authors.forename: Chandra M
authors.initials: CM
authors.lastname: Sehgal
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods / Q000592:standards; D007090:Image Interpretation, Computer-Assisted / Q000379:methods* / Q000592:standards; D008875:Middle Aged; D010363:Pattern Recognition, Automated / Q000379:methods* / Q000592:standards; D017410:Practice Guidelines as Topic; D011379:Prognosis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016037:Single-Blind Method; D016217:Ultrasonography, Mammary / Q000379:methods* / Q000592:standards*
keywords: Bayes probability of malignancy; Breast Imaging Reporting and Data System (BI-RADS); breast cancer imaging; negative predictive value; positive predictive value