A Novel Hybrid Genetic-neural Approach for Breast Cancer Diagnosis on Dynamic Magnetic Resonance Imaging

A hybrid genetic-neural (GA-ANN) model was designed to differentiate malignant from benign in a group of patients with histopathologically proved breast lesions on the base of BI-RADS descriptors and data derived independently from time-intensity curve. We used a database with 117 patients' records each of which consisted of 27 quantitative parameters mostly derived from time-intensity curve, 4 BI-RADS qualitative data which determined by expert radiologist and patient age. These findings were encoded as features for a genetic algorithm (GA) as a preprocessor for feature selection and classified with a three-layered neural network to predict the outcome of biopsy. The network was trained and tested using the jackknife method and its performance was then compared to that of the experienced radiologist in terms of sensitivity, specificity, accuracy and receiver operating characteristic curve (ROC) analysis. The network was able to classify correctly 107 of 117 original cases and yielded a good diagnostic accuracy (91%), sensitivity (95%) and specificity (78%) compared to that of the radiologist (92%), (96%) and (78%).