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. Copyright (c) 2007 ISUOG.
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. Copyright (c) 2007 ISUOG.
Authors: Ben Van Calster; Lil Valentin; Caroline Van Holsbeke; Antonia C Testa; Tom Bourne; Sabine Van Huffel; Dirk Timmerman Journal: BMC Med Res Methodol Date: 2010-10-20 Impact factor: 4.615
Authors: Vanya M C A Van Belle; Ben Van Calster; Dirk Timmerman; Tom Bourne; Cecilia Bottomley; Lil Valentin; Patrick Neven; Sabine Van Huffel; Johan A K Suykens; Stephen Boyd Journal: PLoS One Date: 2012-03-29 Impact factor: 3.240
Authors: Shuihua Wang; Mengmeng Chen; Yang Li; Yudong Zhang; Liangxiu Han; Jane Wu; Sidan Du Journal: Comput Math Methods Med Date: 2015-11-24 Impact factor: 2.238