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

Timmerman, Dirk ; Van Calster, Ben ; Testa, Antonia ; Savelli, Luca ; Fischerova, Daniela ; Froyman, Wouter ; Wynants, Laure ; Van Holsbeke, Caroline ; Epstein, Elisabeth and Franchi, Dorella , et al. (2016) In American Journal of Obstetrics and Gynecology 214(4). p.424-437
Abstract
Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent meta-analysis concluded that the International Ovarian Tumor Analysis (IOTA) algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. 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... (More)
Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent meta-analysis concluded that the International Ovarian Tumor Analysis (IOTA) algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. 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. (Less)
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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
American Journal of Obstetrics and Gynecology
volume
214
issue
4
pages
424 - 437
publisher
Elsevier
external identifiers
  • pmid:26800772
  • pmid:26800772
  • scopus:84975783313
  • wos:000375447400003
ISSN
1097-6868
DOI
10.1016/j.ajog.2016.01.007
language
English
LU publication?
yes
id
f1793b81-77ef-4b96-b376-429e31811ce1 (old id 8574067)
alternative location
http://www.ncbi.nlm.nih.gov/pubmed/26800772?dopt=Abstract
date added to LUP
2016-04-04 07:48:43
date last changed
2022-07-10 03:29:01
@article{f1793b81-77ef-4b96-b376-429e31811ce1,
  abstract     = {{Accurate methods to preoperatively characterize adnexal tumors are pivotal for optimal patient management. A recent meta-analysis concluded that the International Ovarian Tumor Analysis (IOTA) algorithms such as the Simple Rules are the best approaches to preoperatively classify adnexal masses as benign or malignant. 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.<br>
STUDY DESIGN:<br>
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.<br>
RESULTS:<br>
<br>
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 (&lt;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%.<br>
CONCLUSION:<br>
<br>
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.}},
  author       = {{Timmerman, Dirk and Van Calster, Ben and Testa, Antonia and Savelli, Luca and Fischerova, Daniela and Froyman, Wouter and Wynants, Laure and Van Holsbeke, Caroline and Epstein, Elisabeth and Franchi, Dorella and Kaijser, Jeroen and Czekierdowksi, Artur and Guerriero, Stefano and Fruscio, Robert and Leone, Francesco Pg and Rossi, Alberto and Landolfo, Chiara and Vergote, Ignace and Bourne, Tom and Valentin, Lil}},
  issn         = {{1097-6868}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{4}},
  pages        = {{424--437}},
  publisher    = {{Elsevier}},
  series       = {{American Journal of Obstetrics and Gynecology}},
  title        = {{Predicting the risk of malignancy in adnexal masses based on the Simple Rules from the International Ovarian Tumor Analysis (IOTA) group.}},
  url          = {{http://dx.doi.org/10.1016/j.ajog.2016.01.007}},
  doi          = {{10.1016/j.ajog.2016.01.007}},
  volume       = {{214}},
  year         = {{2016}},
}