title: [Analysis of the positive predictive value of the subcategories of BI-RADS(®) 4 lesions: preliminary results in 880 lesions].
abstract: OBJECTIVE
  The positive predictive values (PPV) of the subcategories of BI-RADS(®) 4 lesions (A/B/C) vary widely, and their correlation with specific descriptors has yet to be defined. We aimed to analyze the PPV of the subcategories and of the mammographic and ultrasonographic descriptors assigned to each.
  
  
  METHODS
  We analyzed 880 histologically confirmed lesions prospectively classified as BI-RADS(®) 4 A/B/C between 2003 and 2010. The statistical analysis included significance tests, contingency tables, and relative risk (RR) ratios, calculated for 545 mammographic lesions and 627 ultrasonographic lesions.
  
  
  RESULTS
  The PPV was 8.8% for subcategory 4A, 18.9% for subcategory 4B, and 58.3% for subcategory 4C. The correlation between PPV and lesions was what we expected, with three exceptions: a) the PPV of 4A was greater than that of 4B in nodules that were irregular or had uncircumscribed margins on ultrasonography and in microcalcifications with segmental distribution on mammography, b) BI-RADS(®) 3 lesions classified as BI-RADS(®) 4, and c) identical lesions classified in distinct subcategories. In the contingency table analysis, the mammographic lesions were 4B/C and the ultrasonographic lesions were 4B. On mammography, the RR was significant for nodules with irregular shape (RR=3.205) and for those with spiculated margins (RR=2.469), as well as for microcalcifications that were pleomorphic (RR=2.531) or amorphous (RR=0.334), and for those with segmental (RR=1.895). On ultrasonography, the RR were significant for all the descriptors, with values greater than 1 for irregular shape (RR=1.977) and uncircumscribed margins (RR=2.277).
  
  
  CONCLUSIONS
  Our results corroborate previous reports. The exceptions can be explained by aspects related to variability and nonradiological factors that might influence the classification and PPV. Mathematical models should be developed to enable the objective classification and these should include factors not related to imaging.
authors.affiliation: Unidad de Radiología de la Mujer, Grupo Hospital de Madrid, Madrid, España. mtorrestabanera@gmail.com
authors.forename: M
authors.initials: M
authors.lastname: Torres-Tabanera
authors.forename: J M
authors.initials: JM
authors.lastname: Cárdenas-Rebollo
authors.forename: P
authors.initials: P
authors.lastname: Villar-Castaño
authors.forename: S M
authors.initials: SM
authors.lastname: Sánchez-Gómez
authors.forename: J
authors.initials: J
authors.lastname: Cobo-Soler
authors.forename: E E
authors.initials: EE
authors.lastname: Montoro-Martos
authors.forename: M
authors.initials: M
authors.lastname: Sainz-Miranda
mesh_terms: D001943:Breast Neoplasms / Q000145:classification / Q000000981:diagnostic imaging*; D005260:Female; D006801:Humans; D008327:Mammography; D011237:Predictive Value of Tests; D011446:Prospective Studies; D012189:Retrospective Studies; D014463:Ultrasonography