Document Detail

External validation of diagnostic models to estimate the risk of malignancy in adnexal masses.
MedLine Citation:
PMID:  22114135     Owner:  NLM     Status:  Publisher    
AimTo externally validate and compare the performance of previously published diagnostic models developed to predict malignancy in adnexal masses.MethodsWe 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 -). Results742 (74%) benign and 255 (26%) malignant masses were included. The IOTA models performed better than the non-IOTA models (AUCs between 0.941 and 0.956 versus 0.839 and 0.928). The difference in AUC between the best IOTA and the best non-IOTA model was 0.028 (95% CI, 0.011-0.044). The AUC of the Risk of Malignancy Index (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.ConclusionExternal validation demonstrates that the IOTA models outperform other models -including the current reference test RMI - for discriminating between benign and malignant adnexal masses.
Caroline D Van Holsbeke; Ben Van Calster; Tom Bourne; Silvia Ajossa; Antonia C Testa; Stefano Guerriero; Robert Fruscio; Andrea A Lissoni; Artur Czekierdowski; Luca Savelli; Sabine Van Huffel; Lil Valentin; Dirk Timmerman
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Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2011-11-23
Journal Detail:
Title:  Clinical cancer research : an official journal of the American Association for Cancer Research     Volume:  -     ISSN:  1078-0432     ISO Abbreviation:  -     Publication Date:  2011 Nov 
Date Detail:
Created Date:  2011-11-24     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  9502500     Medline TA:  Clin Cancer Res     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Department of Obstetrics and Gynecology, Ziekenhuis Oost-Limburg, Genk, Belgium.
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