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

J. Kaijser, T. Bourne, Lil Valentin, A. Sayasneh, C. Van Holsbeke, I. Vergote, A. C. Testa, D. Franchi, B. Van Calster, D. Timmerman

Research output: Contribution to journalReview articlepeer-review


In order to ensure that ovarian cancer patients access appropriate treatment to improve the outcome of this disease, accurate characterization before any surgery on ovarian pathology is essential. The International Ovarian Tumor Analysis (IOTA) collaboration has standardized the approach to the ultrasound description of adnexal pathology. A prospectively collected large database enabled previously developed prediction models like the risk of malignancy index (RMI) to be tested and novel prediction models to be developed and externally validated in order to determine the optimal approach to characterize adnexal pathology preoperatively. The main IOTA prediction models (logistic regression model 1 (LR1) and logistic regression model 2 (LR2)) have both shown excellent diagnostic performance (area under the curve (AUC) values of 0.96 and 0.95, respectively) and outperform previous diagnostic algorithms. Their test performance almost matches subjective assessment by experienced examiners, which is accepted to be the best way to classify adnexal masses before surgery. A two-step strategy using the IOTA simple rules supplemented with subjective assessment of ultrasound findings when the rules do not apply, also reached excellent diagnostic performance (sensitivity 90%, specificity 93%) and misclassified fewer malignancies than did the RMI. An evidence-based approach to the preoperative characterization of ovarian and other adnexal masses should include the use of LR1, LR2 or IOTA simple rules and subjective assessment by an experienced examiner. Copyright (c) 2012 ISUOG. Published by John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)9-20
JournalUltrasound in Obstetrics & Gynecology
Issue number1
Publication statusPublished - 2013

Subject classification (UKÄ)

  • Radiology, Nuclear Medicine and Medical Imaging


  • biomarkers
  • decision support techniques
  • logistic models
  • ovarian
  • neoplasms
  • ultrasonography


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