TY - JOUR
T1 - Preoperative diagnosis of ovarian tumors using Bayesian kernel-based methods
AU - van Calster, B.
AU - Timmerman, D.
AU - Lu, C.
AU - Suykens, J. A. K.
AU - Valentin, L.
AU - van Holsbeke, C.
AU - Amant, F.
AU - Vergote, I.
AU - van Huffel, S.
PY - 2007
Y1 - 2007
N2 - 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
AB - 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
U2 - https://doi.org/10.1002/uog.3996
DO - https://doi.org/10.1002/uog.3996
M3 - Article
C2 - 17444557
SN - 0960-7692
VL - 29
SP - 496
EP - 504
JO - Ultrasound in Obstetrics & Gynecology
JF - Ultrasound in Obstetrics & Gynecology
IS - 5
ER -