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Purpose: To evaluate the utility of preoperative diagnostic models for ovarian cancer based on ultrasound and/or biomarkers for referring patients to specialized oncology care. The investigated models were RMI, ROMA, and 3 models from the International Ovarian Tumor Analysis (IOTA) group [LR2, ADNEX, and the Simple Rules risk score (SRRisk)].Experimental Design: A secondary analysis of prospectively collected data from 2 cross-sectional cohort studies was performed to externally validate diagnostic models. A total of 2,763 patients (2,403 in dataset 1 and 360 in dataset 2) from 18 centers (11 oncology centers and 7 nononcology hospitals) in 6 countries participated. Excised tissue was histologically classified as benign or malignant. The clinical utility of the preoperative diagnostic models was assessed with net benefit (NB) at a range of risk thresholds (5%–50% risk of malignancy) to refer patients to specialized oncology care. We visualized results with decision curves and generated bootstrap confidence intervals.Results: The prevalence of malignancy was 41% in dataset 1 and 40% in dataset 2. For thresholds up to 10% to 15%, RMI and ROMA had a lower NB than referring all patients. SRRisks and ADNEX demonstrated the highest NB. At a threshold of 20%, the NBs of ADNEX, SRrisks, and RMI were 0.348, 0.350, and 0.270, respectively. Results by menopausal status and type of center (oncology vs. nononcology) were similar.Conclusions: All tested IOTA methods, especially ADNEX and SRRisks, are clinically more useful than RMI and ROMA to select patients with adnexal masses for specialized oncology care. Clin Cancer Res; 23(17); 5082–90. ©2017 AACR.

Supplementary Table 1. Validated prediction models; Supplementary Table 2. Data contributed to dataset 1 per center; Supplementary Table 3. Net Benefit of the LR2 model by IOTA (LR2), the Risk of Malignancy Index (RMI at cut-off 200), the IOTA Simple Rules Risk scoring system (SRRisks), and the IOTA ADNEX model in all patients; Supplementary Table 4. Difference between the Net Benefit (NB) of a model and that of the best default strategy; Supplementary Table 5. Difference between the Net Benefit of a model and that of the best default strategy; Supplementary Table 6. Difference between the Net Benefit of each model and the Net Benefit of the best model (NB best model - NB model) in premenopausal patients; Supplementary Table 7. Net Benefit of the International Ovarian Tumor Analysis (IOTA) logistic regression model 2 (LR2), the Risk of Malignancy Index (RMI, cut-off 200), and the Risk of Ovarian Malignancy Algorithm (ROMA) (n=360, 40% malignant tumors); Supplementary Table 8. Difference between the Net Benefit (NB) of a model and that of the best default strategy (NB model - NB best default) and between the NB of the best model and that of each of the other models; Supplementary Figure 1. Decision curves representing the Net Benefit of the Risk of Malignancy Index (RMI) with cut-offs 25, 100, 200, 250 and 450, the International Ovarian Tumor Analysis (IOTA) logistic regression model 2 (LR2), and the IOTA ADNEX model (ADNEX) and the ADNEX model without CA125 measurements.