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Title: Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies
Authors: Kaijser, J ×
Bourne, Tom
Valentin, L
Sayasneh, A
Van Holsbeke, Caroline
Vergote, Ignace
Testa, A
Franchi, D
Van Calster, Ben
Timmerman, Dirk #
Issue Date: Jan-2013
Publisher: Blackwell Science
Series Title: Ultrasound in Obstetrics & Gynecology vol:41 issue:1 pages:9-20
Article number: 10.1002/uog.12323
Abstract: 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 both 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 LR1 and LR2 have both shown excellent diagnostic performance (AUCs 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 prior to 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 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 © 2012 ISUOG. Published by John Wiley & Sons, Ltd.
ISSN: 0960-7692
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Gynaecological Oncology
× corresponding author
# (joint) last author

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