Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods
Article first published online: 20 APR 2007
Copyright © 2007 ISUOG. Published by John Wiley & Sons, Ltd.
Ultrasound in Obstetrics & Gynecology
Volume 29, Issue 5, pages 496–504, May 2007
How to Cite
Van Calster, B., Timmerman, D., Lu, C., Suykens, J. A. K., Valentin, L., Van Holsbeke, C., Amant, F., Vergote, I. and Van Huffel, S. (2007), Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods. Ultrasound Obstet Gynecol, 29: 496–504. doi: 10.1002/uog.3996
- Issue published online: 20 APR 2007
- Article first published online: 20 APR 2007
- Manuscript Accepted: 2 JAN 2007
- This research was supported by Research Council KUL: GOA-AMBioRICS, CoE EF/05/006 Optimization in Engineering, several PhD/postdoc & fellow grants, Flemish Government: FWO (research communities (ICCoS, ANMMM)), Belgian Federal Science Policy Office IUAP P5/22 (‘Dynamical Systems and Control: Computation, Identification and Modeling’), EU: BIOPATTERN (FP6-2002-IST 508803), ETUMOUR (FP6-2002-LIFESCIHEALTH 503094), Healthagents (IST–2004-27214), the Swedish Medical Research Council: (grants nos. K2001-72X-11605-06A, K2002-72X-11605-07B and K2004-73X-11605-09A), Swedish governmental grants: (Landstingsfinansierad regional forskning (Region Skåne and ALF-medel)), and Funds administered by Malmö University Hospital
- Bayesian evidence framework;
- least squares support vector machines;
- logistic regression;
- ovarian tumor classification;
- relevance vector machines;
To develop flexible classifiers that predict malignancy in adnexal masses using a large database from nine centers.
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).
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.
Bayesian kernel-based methods can accurately separate malignant from benign masses. The robustness of the models will be investigated in future studies. Copyright © 2007 ISUOG. Published by John Wiley & Sons, Ltd.