A Computerized System to Assess Axillary Lymph Node Malignancy from Sonographic Images.

A computational approach to classifying axillary lymph node metastasis in sonographic images is described. One hundred five ultrasound images of axillary lymph nodes from patients with breast cancer were evaluated (81 benign and 24 malignant), and each lymph node was manually segmented, delineating both the whole lymph node and internal hilum surfaces. Normalized signed distance transforms were computed from the segmented boundaries of both structures, and each pixel was then assigned coordinates in a 3-D feature space according to the pixel's intensity, its signed distance to the node boundary and its signed distance to the hilum boundary. Three-dimensional histograms over the feature space were accumulated for each node by summing over all pixels, and the bin counts served as predictor inputs to a support vector machine learning algorithm. Repeated random sampling of 80/25 train/test splits was used to estimate generalization performance and generate receiver operating characteristic curves. The optimal classifier had an area under the receiver operating characteristic curve of 0.95 and sensitivity and specificity of 0.90 and 0.90. Our results indicate the feasibility of axillary nodal staging with computerized analysis.