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External Validation of Diagnostic Models to Estimate the Risk of Malignancy in Adnexal Masses

Van Holsbeke, Caroline; Van Calster, Ben; Bourne, Tom; Ajossa, Silvia; Testa, Antonia C.; Guerriero, Stefano; Fruscio, Robert; Lissoni, Andrea Alberto; Czekierdowski, Artur and Savelli, Luca, et al. (2012) In Clinical Cancer Research 18(3). p.815-825
Abstract
Purpose: To externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses. Experimental Design: We externally validated the diagnostic performance of 11 models developed by the International Ovarian Tumor Analysis (IOTA) group and 12 other (non-IOTA) models on 997 prospectively collected patients. The non-IOTA models included the original risk of malignancy index (RMI), three modified versions of the RMI, six logistic regression models, and two artificial neural networks. The ability of the models to discriminate between benign and malignant adnexal masses was expressed as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity,... (More)
Purpose: To externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses. Experimental Design: We externally validated the diagnostic performance of 11 models developed by the International Ovarian Tumor Analysis (IOTA) group and 12 other (non-IOTA) models on 997 prospectively collected patients. The non-IOTA models included the original risk of malignancy index (RMI), three modified versions of the RMI, six logistic regression models, and two artificial neural networks. The ability of the models to discriminate between benign and malignant adnexal masses was expressed as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and likelihood ratios (LR+, LR-). Results: Seven hundred and forty-two (74%) benign and 255 (26%) malignant masses were included. The IOTA models did better than the non-IOTA models (AUCs between 0.941 and 0.956 vs. 0.839 and 0.928). The difference in AUC between the best IOTA and the best non-IOTA model was 0.028 [95% confidence interval (CI), 0.011-0.044]. The AUC of the RMI was 0.911 (difference with the best IOTA model, 0.044; 95% CI, 0.024-0.064). The superior performance of the IOTA models was most pronounced in premenopausal patients but was also observed in postmenopausal patients. IOTA models were better able to detect stage I ovarian cancer. Conclusion: External validation shows that the IOTA models outperform other models, including the current reference test RMI, for discriminating between benign and malignant adnexal masses. Clin Cancer Res; 18(3); 815-25. (C)2011 AACR. (Less)
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Clinical Cancer Research
volume
18
issue
3
pages
815 - 825
publisher
American Association for Cancer Research
external identifiers
  • wos:000300115000025
  • scopus:84856557924
ISSN
1078-0432
DOI
10.1158/1078-0432.CCR-11-0879
language
English
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yes
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eb22a876-e023-4f47-86bc-7c7621c920bc (old id 2409575)
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2012-04-02 09:30:43
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@article{eb22a876-e023-4f47-86bc-7c7621c920bc,
  abstract     = {Purpose: To externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses. Experimental Design: We externally validated the diagnostic performance of 11 models developed by the International Ovarian Tumor Analysis (IOTA) group and 12 other (non-IOTA) models on 997 prospectively collected patients. The non-IOTA models included the original risk of malignancy index (RMI), three modified versions of the RMI, six logistic regression models, and two artificial neural networks. The ability of the models to discriminate between benign and malignant adnexal masses was expressed as the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and likelihood ratios (LR+, LR-). Results: Seven hundred and forty-two (74%) benign and 255 (26%) malignant masses were included. The IOTA models did better than the non-IOTA models (AUCs between 0.941 and 0.956 vs. 0.839 and 0.928). The difference in AUC between the best IOTA and the best non-IOTA model was 0.028 [95% confidence interval (CI), 0.011-0.044]. The AUC of the RMI was 0.911 (difference with the best IOTA model, 0.044; 95% CI, 0.024-0.064). The superior performance of the IOTA models was most pronounced in premenopausal patients but was also observed in postmenopausal patients. IOTA models were better able to detect stage I ovarian cancer. Conclusion: External validation shows that the IOTA models outperform other models, including the current reference test RMI, for discriminating between benign and malignant adnexal masses. Clin Cancer Res; 18(3); 815-25. (C)2011 AACR.},
  author       = {Van Holsbeke, Caroline and Van Calster, Ben and Bourne, Tom and Ajossa, Silvia and Testa, Antonia C. and Guerriero, Stefano and Fruscio, Robert and Lissoni, Andrea Alberto and Czekierdowski, Artur and Savelli, Luca and Van Huffel, Sabine and Valentin, Lil and Timmerman, Dirk},
  issn         = {1078-0432},
  language     = {eng},
  number       = {3},
  pages        = {815--825},
  publisher    = {American Association for Cancer Research},
  series       = {Clinical Cancer Research},
  title        = {External Validation of Diagnostic Models to Estimate the Risk of Malignancy in Adnexal Masses},
  url          = {http://dx.doi.org/10.1158/1078-0432.CCR-11-0879},
  volume       = {18},
  year         = {2012},
}