title: Use of ultrasound pattern recognition by expert operators to identify borderline ovarian tumors: a study of diagnostic performance and interobserver agreement.
abstract: OBJECTIVE
  To assess the accuracy and reproducibility of ultrasound 'pattern recognition' for the diagnosis of borderline ovarian tumors by asking experienced ultrasound operators to evaluate representative images of different types of adnexal tumor.
  
  
  METHODS
  Digitally stored static two-dimensional B-mode images of representative cases of benign, borderline and invasive malignant ovarian tumors were independently assessed by three expert sonologists who had not performed the original real-time ultrasound examination. The outcome measures included diagnostic accuracy and interobserver agreement in the diagnosis of benign, borderline or invasive malignant ovarian tumors.
  
  
  RESULTS
  One hundred and sixty-six cases were included in the final data analysis. A correct classification was made by all three experts in 83% of the primary invasive cancers, 76% of the benign masses and in 44% of the borderline malignant tumors (P < 0.01). The experts showed a tendency to misclassify borderline tumors as benign rather than primary invasive (ratio of 8 : 1 for Expert A, 4 : 1 for B and 6 : 1 for C). The interobserver agreement between any two experts was very good when they were tested for their ability to discriminate between invasive and non-invasive (benign and borderline) ovarian tumors (Cohen's kappa 0.85-0.88), but poorer for the discrimination between malignant (invasive and borderline) and benign tumors (kappa 0.70-0.78).
  
  
  CONCLUSIONS
  The accuracy of ultrasound diagnosis of borderline tumors is lower in comparison with benign and invasive malignant lesions. The diagnostic performance and interobserver agreement are better when the outcomes are dichotomized into non-invasive and invasive malignant lesions, as opposed to the traditional diagnosis of benign and malignant tumors.
authors.affiliation: Early Pregnancy and Gynaecology Assessment Unit, King's College Hospital, London, UK. davor.jurkovic@uclh.nhs.uk
authors.forename: J
authors.initials: J
authors.lastname: Yazbek
authors.forename: L
authors.initials: L
authors.lastname: Ameye
authors.forename: D
authors.initials: D
authors.lastname: Timmerman
authors.forename: A C
authors.initials: AC
authors.lastname: Testa
authors.forename: L
authors.initials: L
authors.lastname: Valentin
authors.forename: T K
authors.initials: TK
authors.lastname: Holland
authors.forename: C
authors.initials: C
authors.lastname: Van Holsbeke
authors.forename: D
authors.initials: D
authors.lastname: Jurkovic
mesh_terms: D000291:Adnexal Diseases / Q000000981:diagnostic imaging*; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D002983:Clinical Competence; D005260:Female; D006801:Humans; D008875:Middle Aged; D009367:Neoplasm Staging; D015588:Observer Variation; D010051:Ovarian Neoplasms / Q000000981:diagnostic imaging*; D010364:Pattern Recognition, Visual; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D014463:Ultrasonography; D055815:Young Adult