title: Benign and malignant ovarian masses: selection of the most discriminating gray-scale and Doppler sonographic features.
abstract: OBJECTIVE
  To determine the gray-scale and Doppler sonographic features that best enable discrimination between malignant and benign ovarian masses and develop a scoring system for accurate diagnosis with these features.
  
  
  METHODS
  Gray-scale and Doppler sonographic features of 211 adnexal masses were correlated with the final diagnosis; the most discriminating features for malignancy were selected with stepwise logistic regression.
  
  
  RESULTS
  Twenty-eight masses were malignant and 183 benign. All masses with a markedly hyperechoic solid component or no solid component were benign. For masses with a nonhyperechoic solid component, additional features that allowed statistically significant discrimination of benignity from malignancy were, in decreasing order of importance, (a) location of flow at conventional color Doppler imaging, (b) amount of free intraperitoneal fluid, and (c) presence and thickness of septations. A scoring formula that made use of values based on the logistic regression equation had an area under the receiver operating characteristic curve of 0.98 +/- 0.01. The cutoff score with the highest accuracy had a sensitivity of 93% and specificity of 93%.
  
  
  CONCLUSIONS
  A solid component is the most statistically significant predictor of a malignant ovarian mass. A multiparameter scoring system that uses three gray-scale and one Doppler feature, developed by means of stepwise logistic regression, has high sensitivity and specificity for predicting malignancy.
authors.affiliation: Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
authors.forename: D L
authors.initials: DL
authors.lastname: Brown
authors.forename: P M
authors.initials: PM
authors.lastname: Doubilet
authors.forename: F H
authors.initials: FH
authors.lastname: Miller
authors.forename: M C
authors.initials: MC
authors.lastname: Frates
authors.forename: F C
authors.initials: FC
authors.lastname: Laing
authors.forename: D N
authors.initials: DN
authors.lastname: DiSalvo
authors.forename: C B
authors.initials: CB
authors.lastname: Benson
authors.forename: M H
authors.initials: MH
authors.lastname: Lerner
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D001202:Ascitic Fluid / Q000000981:diagnostic imaging; D016009:Chi-Square Distribution; D003536:Cystadenocarcinoma / Q000000981:diagnostic imaging; D003937:Diagnosis, Differential; D004715:Endometriosis / Q000000981:diagnostic imaging; D005260:Female; D005544:Forecasting; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D010048:Ovarian Cysts / Q000000981:diagnostic imaging; D010049:Ovarian Diseases / Q000000981:diagnostic imaging* / Q000473:pathology; D010051:Ovarian Neoplasms / Q000098:blood supply / Q000000981:diagnostic imaging* / Q000473:pathology; D010053:Ovary / Q000000981:diagnostic imaging; D011446:Prospective Studies; D012372:ROC Curve; D012680:Sensitivity and Specificity; D013724:Teratoma / Q000000981:diagnostic imaging; D018608:Ultrasonography, Doppler; D018615:Ultrasonography, Doppler, Color