Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis group

BACKGROUND: Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent metaanalysis concluded that the International Ovarian Tumor Analysis algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. OBJECTIVE: We sought to develop and validate a model to predict the risk of malignancy in adnexal masses using the ultrasound features in the Simple Rules. STUDY DESIGN: This was an international cross-sectional cohort study involving 22 oncology centers, referral centers for ultrasonography, and general hospitals. We included consecutive patients with an adnexal tumor who underwent a standardized transvaginal ultrasound examination and were selected for surgery. Data on 5020 patients were recorded in 3 phases from 2002 through 2012. The 5 Simple Rules features indicative of a benign tumor (B-features) and the 5 features indicative of malignancy (M-features) are based on the presence of ascites, tumor morphology, and degree of vascularity at ultrasonography. Gold standard was the histopathologic diagnosis of the adnexal mass (pathologist blinded to ultrasound findings). Logistic regression analysis was used to estimate the risk of malignancy based on the 10 ultrasound features and type of center. The diagnostic performance was evaluated by area under the receiver operating characteristic curve, sensitivity, specificity, positive likelihood ratio (LR+), negative likelihood ratio (LR-), positive predictive value (PPV), negative predictive value (NPV), and calibration curves. RESULTS: Data on 4848 patients were analyzed. The malignancy rate was 43% (1402/3263) in oncology centers and 17% (263/1585) in other centers. The area under the receiver operating characteristic curve on validation data was very similar in oncology centers (0.917; 95% confidence interval, 0.901-0.931) and other centers (0.916; 95% confidence interval, 0.873-0.945). Risk estimates showed good calibration. In all, 23% of patients in the validation data set had a very low estimated risk (<1%) and 48% had a high estimated risk (≥30%). For the 1% risk cutoff, sensitivity was 99.7%, specificity 33.7%, LR+ 1.5, LR- 0.010, PPV 44.8%, and NPV 98.9%. For the 30% risk cutoff, sensitivity was 89.0%, specificity 84.7%, LR+ 5.8, LR- 0.13, PPV 75.4%, and NPV 93.9%. CONCLUSION: Quantification of the risk of malignancy based on the Simple Rules has good diagnostic performance both in oncology centers and other centers. A simple classification based on these risk estimates may form the basis of a clinical management system. Patients with a high risk may benefit from surgery by a gynecological oncologist, while patients with a lower risk may be managed locally

SNCG (synuclein, gamma (breast cancer-specific protein 1))

Review on SNCG (synuclein, gamma (breast cancer-specific protein 1)), with data on DNA, on the protein encoded, and where the gene is implicated

Strategies to diagnose ovarian cancer: new evidence from phase 3 of the multicentre international IOTA study

Background: To compare different ultrasound-based international ovarian tumour analysis (IOTA) strategies and risk of malignancy index (RMI) for ovarian cancer diagnosis using a meta-analysis approach of centre-specific data from IOTA3. Methods: This prospective multicentre diagnostic accuracy study included 2403 patients with 1423 benign and 980 malignant adnexal masses from 2009 until 2012. All patients underwent standardised transvaginal ultrasonography. Test performance of RMI, subjective assessment (SA) of ultrasound findings, two IOTA risk models (LR1 and LR2), and strategies involving combinations of IOTA simple rules (SRs), simple descriptors (SDs) and LR2 with and without SA was estimated using a meta-analysis approach. Reference standard was histology after surgery. Results: The areas under the receiver operator characteristic curves of LR1, LR2, SA and RMI were 0.930 (0.917–0.942), 0.918 (0.905–0.930), 0.914 (0.886–0.936) and 0.875 (0.853–0.894). Diagnostic one-step and two-step strategies using LR1, LR2, SR and SD achieved summary estimates for sensitivity 90–96%, specificity 74–79% and diagnostic odds ratio (DOR) 32.8–50.5. Adding SA when IOTA methods yielded equivocal results improved performance (DOR 57.6–75.7). Risk of Malignancy Index had sensitivity 67%, specificity 91% and DOR 17.5. Conclusions: This study shows all IOTA strategies had excellent diagnostic performance in comparison with RMI. The IOTA strategy chosen may be determined by clinical preference

OC152: Prevalence of cancer and optimal cut‐off levels for mathematical models to distinguish between benign and malignant adnexal masses

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