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Title: Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods
Authors: Van Calster, Ben ×
Timmerman, Dirk
Lu, C
Suykens, Johan
Valentine, L
Van Holsbeke, C
Amant, Frédéric
Vergote, Ignace
Van Huffel, Sabine #
Issue Date: May-2007
Publisher: John wiley & sons ltd
Series Title: Ultrasound in Obstetrics & Gynecology vol:29 issue:5 pages:496-504
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.
URI: 
ISSN: 0960-7692
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Basic Research in Gynaecology Section (-)
Gynaecological Imaging Section (-)
Gynaecological Oncology
ESAT - STADIUS, Stadius Centre for Dynamical Systems, Signal Processing and Data Analytics
× corresponding author
# (joint) last author

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