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dc.contributor.author Van Holsbeke, Caroline
dc.contributor.author Van Calster, Ben
dc.contributor.author Testa, Antonia C.
dc.contributor.author Domali, Ekaterini
dc.contributor.author Lu, Chuan
dc.contributor.author Van Huffel, Sabine
dc.contributor.author Valentin, Lil
dc.contributor.author Timmerman, Dirk
dc.date.accessioned 2010-02-25T17:41:02Z
dc.date.available 2010-02-25T17:41:02Z
dc.date.issued 2009-01-15
dc.identifier.citation Van Holsbeke , C , Van Calster , B , Testa , A C , Domali , E , Lu , C , Van Huffel , S , Valentin , L & Timmerman , D 2009 , ' Prospective internal validation of mathematical models to predict malignancy in adnexal masses: results from the International Ovarian Tumor Analysis (IOTA) Study ' Clinical Cancer Research , vol 15 , no. 684 , pp. 684-691 . , 10.1158/1078-0432.CCR-08-0113 en
dc.identifier.issn 1078-0432
dc.identifier.other PURE: 144418
dc.identifier.other dspace: 2160/4101
dc.identifier.uri http://hdl.handle.net/2160/4101
dc.identifier.uri http://clincancerres.aacrjournals.org/content/15/2/684.long en
dc.description Van Holsbeke, C., Van Calster, B., Testa, A. C., Domali, E., Lu, C., Van Huffel, S., Valentin, L., Timmerman, D. (2009). Prospective internal validation of mathematical models to predict malignancy in adnexal masses: results from the International Ovarian Tumor Analysis (IOTA) Study. Clinical Cancer Research, 15 (684), 684-691 Sponsorship: MeTRO en
dc.description.abstract PURPOSE: To prospectively test the mathematical models for calculation of the risk of malignancy in adnexal masses that were developed on the International Ovarian Tumor Analysis (IOTA) phase 1 data set on a new data set and to compare their performance with that of pattern recognition, our standard method. METHODS: Three IOTA centers included 507 new patients who all underwent a transvaginal ultrasound using the standardized IOTA protocol. The outcome measure was the histologic classification of excised tissue. The diagnostic performance of 11 mathematical models that had been developed on the phase 1 data set and of pattern recognition was expressed as area under the receiver operating characteristic curve (AUC) and as sensitivity and specificity when using the cutoffs recommended in the studies where the models had been created. For pattern recognition, an AUC was made based on level of diagnostic confidence. RESULTS: All IOTA models performed very well and quite similarly, with sensitivity and specificity ranging between 92% and 96% and 74% and 84%, respectively, and AUCs between 0.945 and 0.950. A least squares support vector machine with linear kernel and a logistic regression model had the largest AUCs. For pattern recognition, the AUC was 0.963, sensitivity was 90.2%, and specificity was 92.9%. CONCLUSION: This internal validation of mathematical models to estimate the malignancy risk in adnexal tumors shows that the IOTA models had a diagnostic performance similar to that in the original data set. Pattern recognition used by an expert sonologist remains the best method, although the difference in performance between the best mathematical model is not large. en
dc.format.extent 8 en
dc.language.iso eng
dc.relation.ispartof Clinical Cancer Research en
dc.subject ultrasonography en
dc.subject ovarian neoplasms en
dc.subject color Doppler sonography en
dc.subject logistic models en
dc.title Prospective internal validation of mathematical models to predict malignancy in adnexal masses: results from the International Ovarian Tumor Analysis (IOTA) Study en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.identifier.doi http://dx.doi.org/10.1158/1078-0432.CCR-08-0113
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Peer reviewed en


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