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

Kaijser, J.; Bourne, T.; Valentin, Lil LU ; Sayasneh, A.; Van Holsbeke, C.; Vergote, I.; Testa, A. C.; Franchi, D.; Van Calster, B. and Timmerman, D. (2013) In Ultrasound in Obstetrics & Gynecology 41(1). p.9-20
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 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))... (More)
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. (Less)
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author
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
biomarkers, decision support techniques, logistic models, ovarian, neoplasms, ultrasonography
in
Ultrasound in Obstetrics & Gynecology
volume
41
issue
1
pages
9 - 20
publisher
John Wiley & Sons
external identifiers
  • wos:000312738900005
  • scopus:84871571350
ISSN
1469-0705
DOI
10.1002/uog.12323
language
English
LU publication?
yes
id
e279ba74-0141-4ce2-81ad-10427225fa5c (old id 3481477)
date added to LUP
2013-03-01 07:49:49
date last changed
2019-10-13 03:59:18
@article{e279ba74-0141-4ce2-81ad-10427225fa5c,
  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 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.},
  author       = {Kaijser, J. and Bourne, T. and Valentin, Lil and Sayasneh, A. and Van Holsbeke, C. and Vergote, I. and Testa, A. C. and Franchi, D. and Van Calster, B. and Timmerman, D.},
  issn         = {1469-0705},
  keyword      = {biomarkers,decision support techniques,logistic models,ovarian,neoplasms,ultrasonography},
  language     = {eng},
  number       = {1},
  pages        = {9--20},
  publisher    = {John Wiley & Sons},
  series       = {Ultrasound in Obstetrics & Gynecology},
  title        = {Improving strategies for diagnosing ovarian cancer: a summary of the International Ovarian Tumor Analysis (IOTA) studies},
  url          = {http://dx.doi.org/10.1002/uog.12323},
  volume       = {41},
  year         = {2013},
}