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 (Az) under the corresponding ROC curves were used as measures of performance. The network incorporating only image based features performed better (Az =0.81) than that using nonimage features (Az = 0.71). Both hybrid classifiers yielded better performance (Az =0.85 for averaging and Az = 0.87 for logistic regression), but neither hybrid was as accurate as the network incorporating all features (Az = 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.