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Comparison of the Risk of Malignancy Index and Self-Constructed Logistic Regression Models in Preoperative Evaluation of Adnexal Masses

Pnar Yörük, MD, Özgür Dündar, MD, Begüm Yldzhan, MD, Levent Tütüncü, MD and Tanju Pekin, MD

Department of Obstetrics and Gynecology, Marmara University, Istanbul, Turkey (P.Y., B.Y., T.P.); and Department of Obstetrics and Gynecology, Gülhane Military Medical Academy Haydarpaa Training Hospital, Ankara, Turkey (Ö.D., L.T.).

Address correspondence to Pnar Yörük, MD, Bor Yolu, Fatih Sitesi B Blok 15, Nide, Turkey. E-mail: pinyoruk{at}gmail.com

Objective. The aim of this study was to evaluate women with adnexal masses in the preoperative period by creating 2 logistic regression models, 1 including sonographic morphologic characteristics and the other including both morphologic and color Doppler characteristics, to compare the diagnostic accuracy of these 2 models with the risk of malignancy index (RMI). Methods. This prospective study included 38 malignant, 7 borderline, and 244 benign ovarian masses. The menopausal status, presence of septa, presence of papillary projections, location of the tumor, presence of ascites, presence of metastases, cancer antigen 125 level, tumor volume, septa thickness, and percentage of the solid component were included in the initial analysis. A second regression analysis was performed with the addition of Doppler parameters (location of blood flow and lowest resistive index) in the data set. Diagnostic performance of the 2 regression models and RMI were described and compared by generating receiver operating characteristic curves for each model. Results. The area under the curve values for the morphologic model (model 1), Doppler model (model 2), and RMI were 0.907, 0.971, and 0.889, respectively. Significance levels of model 1 and the RMI were similar (P = .23), whereas model 2 had a significantly higher area under the curve compared with both model 1 (P = .037) and the RMI (P = .018). Conclusions. The addition of Doppler parameters in the regression model significantly increases the predictive performance. Nevertheless, in low-resource settings, the RMI remains the method of choice for distinguishing adnexal masses and referral to gynecologic oncology clinics

Key Words: adnexal mass • preoperative evaluation • risk-of-malignancy index

Abbreviations: AUC, area under the curve • BMI, body mass index • CA-125, cancer antigen 125 • IOTA, International Ovarian Tumor Analysis • PI, pulsatility index • PSV, peak systolic velocity • RI, resistive index • RMI, risk of malignancy index