Show simple item record Van Calster, Ben Timmerman, Dirk Lu, Chuan Suykens, Johan A. K. Valentin, Lil Van Holsbeke, Caroline Amant, Frédéric Vergote, Ignace Van Huffel, Sabine 2010-02-25T18:12:20Z 2010-02-25T18:12:20Z 2007-05
dc.identifier.citation Van Calster , B , Timmerman , D , Lu , C , Suykens , J A K , Valentin , L , Van Holsbeke , C , Amant , F , Vergote , I & Van Huffel , S 2007 , ' Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods ' Ultrasound in Obstetrics and Gynecology , vol 29 , no. 5 , pp. 496-504 . , 10.1002/uog.3996 en
dc.identifier.issn 1469-0705
dc.identifier.other PURE: 144450
dc.identifier.other dspace: 2160/4102
dc.identifier.uri en
dc.description B. Van Calster, D. Timmerman, C. Lu, J.A.K. Suykens, L. Valentin, C. Van Holsbeke, F. Amant, I. Vergote, S. Van Huffel. Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods, Untrasound in Obstetrics and Gynecology, vol. 29, no. 5, 2007, pp. 496-504. Sponsorship: BIOPATTERN, eTUMOR, MetRO en
dc.description.abstract Objectives To develop flexible classifiers that predict malignancy in adnexal masses using a large database from nine centers. Methods The database consisted of 1066 patients with at least one persistent adnexal mass for which a large amount of clinical and ultrasound data were recorded. The outcome of interest was the histological classification of the adnexal mass as benign or malignant. The outcome was predicted using Bayesian least squares support vector machines in comparison with relevance vector machines. The models were developed on a training set (n = 754) and tested on a test set (n = 312). Results Twenty-five percent of the patients (n = 266) had a malignant tumor. Variable selection resulted in a set of 12 variables for the models: age, maximal diameter of the ovary, maximal diameter of the solid component, personal history of ovarian cancer, hormonal therapy, very strong intratumoral blood flow (i.e. color score 4), ascites, presumed ovarian origin of tumor, multilocular-solid tumor, blood flow within papillary projections, irregular internal cyst wall and acoustic shadows. Test set area under the receiver-operating characteristics curve (AUC) for all models exceeded 0.940, with a sensitivity above 90% and a specificity above 80% for all models. The least squares support vector machine model with linear kernel performed very well, with an AUC of 0.946, 91% sensitivity and 84% specificity. The models performed well in the test sets of all the centers. Conclusions Bayesian kernel-based methods can accurately separate malignant from benign masses. The robustness of the models will be investigated in future studies. en
dc.format.extent 9 en
dc.language.iso eng
dc.relation.ispartof Ultrasound in Obstetrics and Gynecology en
dc.subject Bayesian evidence framework en
dc.subject least squares support vector machines en
dc.subject logistic regression en
dc.subject ovarian tumor classification en
dc.subject relevance vector machines en
dc.subject ultrasound en
dc.title Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods en
dc.type Text en
dc.type.publicationtype Article (Journal) en
dc.contributor.institution Department of Computer Science en
dc.contributor.institution Bioinformatics and Computational Biology Group en
dc.description.status Peer reviewed en

Files in this item

Files Size Format View

There are no files associated with this item.

This item appears in the following Collection(s)

Show simple item record

Search Cadair

Advanced Search