Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies.

摘要: In order to ensure that ovarian cancer patients access appropriate treatment improve the outcome of this disease, accurate characterization before any surgery on pathology is essential. The International Ovarian Tumor Analysis (IOTA) collaboration has standardized approach ultrasound description adnexal pathology. A prospectively collected large database enabled previously developed prediction models like risk malignancy index (RMI) be tested and novel externally validated in determine optimal characterize preoperatively. main IOTA (logistic regression model 1 (LR1) logistic 2 (LR2)) have both shown excellent diagnostic performance (area under curve (AUC) values 0.96 0.95, respectively) outperform previous algorithms. Their test almost matches subjective assessment by experienced examiners, which accepted best way classify masses surgery. two-step strategy using simple rules supplemented with findings when do not apply, also reached (sensitivity 90%, specificity 93%) misclassified fewer malignancies than did RMI. An evidence-based preoperative other should include use LR1, LR2 or an examiner. Copyright (c) 2012 ISUOG. Published John Wiley & Sons, Ltd.