Combined Naïve Bayes and logistic regression for quantitative breast sonography

Sonography is commonly used as an adjunct to mammography for early detection of breast cancer. We are developing methods to classify solid breast masses in sonograms as malignant or benign. The goal of this study was to combine two independent probabilistic classifiers to improve computer-aided diagnosis of breast masses. Naïve Bayes and logistic regression were used for supervised classification of masses from extracted morphological sonographic features, in combination with mammographic BI-RADS (categories 1 to 5) and patient age. Solid masses with biopsy-proven diagnoses were analyzed. Training and testing were performed using leave-one-out cross validation. Diagnostic performance was evaluated by the area under the curve (AUC) of the receiver operating characteristic (ROC). Agreement between predictions from the two classifiers was used to differentiate benign and malignant masses. The results show that logistic regression and Naïve Bayes performed with ROC area of 0.902 ± 0.023 and 0.865 ± 0.027, respectively. The combined use of logistic regression and Naïve Bayes demonstrated reduction in biopsies by 48%, with malignancy missed in 2% of cases (false negative rate of 6.4%).