Computer-assisted diagnosis of breast cancer using a data-driven Bayesian belief network

This study investigates a simple Bayesian belief network for the diagnosis of breast cancer, and specifically addresses the question of whether integrating image and non-image based features into a single network can yield better performance than hybrid combinations of independent networks, From a dataset of 419 cases, including 92 malignancies, 13 features relating to mammographic findings, physical examinations and patients' clinical histories, were extracted to build three Bayesian belief networks. The: scenarios tested included a network incorporating: all features and two hybrids which combined the outputs of sub-networks corresponding to the image or non-image features. Average areas (A(z)) under the corresponding ROC curves were used as measures of performance. The network incorporating only image based features performed better (A(z)=0.81) than that using nonimage features (A(z) = 0.71). Both hybrid classifiers yielded better performance (A(z) = 0.85 for averaging and A(z) = 0.87 for logistic regression), but neither hybrid was as accurate as the network incorporating all features (A(z) = 0.89). This preliminary study suggests that, like human observers who concurrently consider different types of information, a single classifier that simultaneously evaluates both image and non-image information can achieve better diagnostic performance than the hybrid combinations considered here. (C) 1999 Elsevier Science Ireland Ltd. All rights reserved.