title: Characterization of sonographically indeterminate ovarian tumors with MR imaging. A logistic regression analysis.
abstract: OBJECTIVE
  The goal of this study was to maximize the discrimination between benign and malignant masses in patients with sonographically indeterminate ovarian lesions by means of unenhanced and contrast-enhanced MR imaging, and to develop a computer-assisted diagnosis system.
  
  
  METHODS
  Findings in precontrast and Gd-DTPA contrast-enhanced MR images of 104 patients with 115 sonographically indeterminate ovarian masses were analyzed, and the results were correlated with histopathological findings. Of 115 lesions, 65 were benign (23 cystadenomas, 13 complex cysts, 11 teratomas, 6 fibrothecomas, 12 others) and 50 were malignant (32 ovarian carcinomas, 7 metastatic tumors of the ovary, 4 carcinomas of the fallopian tubes, 7 others). A logistic regression analysis was performed to discriminate between benign and malignant lesions, and a model of a computer-assisted diagnosis was developed. This model was prospectively tested in 75 cases of ovarian tumors found at other institutions.
  
  
  RESULTS
  From the univariate analysis, the following parameters were selected as significant for predicting malignancy (p< or =0.05): a solid or cystic mass with a large solid component or wall thickness greater than 3 mm; complex internal architecture; ascites; and bilaterality. Based on these parameters, a model of a computer-assisted diagnosis system was developed with the logistic regression analysis. To distinguish benign from malignant lesions, the maximum cut-off point was obtained between 0.47 and 0.51. In a prospective application of this model, 87% of the lesions were accurately identified as benign or malignant.
  
  
  CONCLUSIONS
  Benign and malignant ovarian lesions can be distinguished in most sonographically indeterminate lesions by means of parameters obtained from contrast-enhanced MR imaging.
authors.affiliation: Department of Radiology, Kumamoto University School of Medicine, Honjo, Japan.
authors.forename: Y
authors.initials: Y
authors.lastname: Yamashita
authors.forename: Y
authors.initials: Y
authors.lastname: Hatanaka
authors.forename: M
authors.initials: M
authors.lastname: Torashima
authors.forename: M
authors.initials: M
authors.lastname: Takahashi
authors.forename: K
authors.initials: K
authors.lastname: Miyazaki
authors.forename: H
authors.initials: H
authors.lastname: Okamura
mesh_terms: D003287:Contrast Media; D003936:Diagnosis, Computer-Assisted; D005260:Female; D005682:Gadolinium; D019786:Gadolinium DTPA; D006801:Humans; D016015:Logistic Models; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008875:Middle Aged; D009942:Organometallic Compounds; D010049:Ovarian Diseases / Q000175:diagnosis / Q000000981:diagnostic imaging / Q000453:epidemiology; D010051:Ovarian Neoplasms / Q000175:diagnosis* / Q000000981:diagnostic imaging / Q000453:epidemiology; D010053:Ovary / Q000473:pathology; D004369:Pentetic Acid / Q000031:analogs & derivatives; D011237:Predictive Value of Tests; D014463:Ultrasonography
chemical_list: D003287:Contrast Media; D009942:Organometallic Compounds; D004369:Pentetic Acid; D005682:Gadolinium; D019786:Gadolinium DTPA