title: Logistic regression model to distinguish between the benign and malignant adnexal mass before surgery: a multicenter study by the International Ovarian Tumor Analysis Group.
abstract: OBJECTIVE
  To collect data for the development of a more universally useful logistic regression model to distinguish between a malignant and benign adnexal tumor before surgery.
  
  
  METHODS
  Patients had at least one persistent mass. More than 50 clinical and sonographic end points were defined and recorded for analysis. The outcome measure was the histologic classification of excised tissues as malignant or benign.
  
  
  RESULTS
  Data from 1,066 patients recruited from nine European centers were included in the analysis; 800 patients (75%) had benign tumors and 266 (25%) had malignant tumors. The most useful independent prognostic variables for the logistic regression model were as follows: (1) personal history of ovarian cancer, (2) hormonal therapy, (3) age, (4) maximum diameter of lesion, (5) pain, (6) ascites, (7) blood flow within a solid papillary projection, (8) presence of an entirely solid tumor, (9) maximal diameter of solid component, (10) irregular internal cyst walls, (11) acoustic shadows, and (12) a color score of intratumoral blood flow. The model containing all 12 variables (M1) gave an area under the receiver operating characteristic curve of 0.95 for the development data set (n = 754 patients). The corresponding value for the test data set (n = 312 patients) was 0.94; and a probability cutoff value of .10 gave a sensitivity of 93% and a specificity of 76%.
  
  
  CONCLUSIONS
  Because the model was constructed from multicenter data, it is more likely to be generally applicable. The effectiveness of the model will be tested prospectively at different centers.
authors.affiliation: Department of Obstetrics and Gynecology, University Hospitals, Katholieke Universiteit Leuven, Herestraat 49, B-3000 Leuven, Belgium. dirk.timmerman@uz.kuleuven.ac.be
authors.forename: Dirk
authors.initials: D
authors.lastname: Timmerman
authors.forename: Antonia C
authors.initials: AC
authors.lastname: Testa
authors.forename: Tom
authors.initials: T
authors.lastname: Bourne
authors.forename: Enrico
authors.initials: E
authors.lastname: Ferrazzi
authors.forename: Lieveke
authors.initials: L
authors.lastname: Ameye
authors.forename: Maja L
authors.initials: ML
authors.lastname: Konstantinovic
authors.forename: Ben
authors.initials: B
authors.lastname: Van Calster
authors.forename: William P
authors.initials: WP
authors.lastname: Collins
authors.forename: Ignace
authors.initials: I
authors.lastname: Vergote
authors.forename: Sabine
authors.initials: S
authors.lastname: Van Huffel
authors.forename: Lil
authors.initials: L
authors.lastname: Valentin
mesh_terms: D000291:Adnexal Diseases / Q000145:classification / Q000175:diagnosis* / Q000601:surgery; D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D014408:Biomarkers, Tumor / Q000097:blood; D018394:CA-125 Antigen / Q000097:blood; D018291:Cystadenoma, Mucinous / Q000145:classification / Q000175:diagnosis / Q000601:surgery; D018292:Cystadenoma, Papillary / Q000145:classification / Q000175:diagnosis / Q000601:surgery; D018293:Cystadenoma, Serous / Q000145:classification / Q000175:diagnosis / Q000601:surgery; D003937:Diagnosis, Differential; D005260:Female; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D015999:Multivariate Analysis; D010048:Ovarian Cysts / Q000145:classification / Q000175:diagnosis / Q000601:surgery; D010051:Ovarian Neoplasms / Q000145:classification / Q000175:diagnosis / Q000601:surgery; D010052:Ovariectomy; D011300:Preoperative Care / Q000706:statistics & numerical data*; D011446:Prospective Studies; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D014408:Biomarkers, Tumor; D018394:CA-125 Antigen