Characterizing case-based repeatability of lesion classification in computer-aided diagnosis (CADx) can support clinical application of CADx in breast cancer imaging. A database of 516 full-field digital mammographic (FFDM) images, 268 unique breast lesions/cases (138 malignant, 130 benign) was retrospectively collected under HIPAA/IRB approval. Lesions were automatically segmented through a region-growing method. Twenty-nine radiomic features were extracted, characterizing lesion size, shape, margin, texture, and contrast. A 0.632 bootstrap/1000 iterations was used to divide cases into training and test folds for support vector machine classification as malignant or benign. Classifier output was averaged by case. The 0.632+ bootstrap-corrected area under the receiver operating characteristic (ROC) curve (AUC) in the task of classification of lesions as malignant or benign was determined. Classifier repeatability was determined as a function of median classifier output across all cases obtained in bootstraps sorted into 10 equal bins. 95% confidence intervals (CI) of sensitivity and specificity were examined as a function of classifier output, i.e., the decision variable in ROC analysis. Actual sensitivity and specificity in the test folds were determined for a target sensitivity (0.95), a target specificity (0.95), and an ‘optimal’ combination that minimized (1-sensitivity)2 + (1-specificity)2. The AUC [95% CI] was 0.82 [0.77, 0.86]. Classifier output was more repeatable at lower and upper ranges. Sensitivity and specificity at the targets were (median, [95% CI]) at 0.92 [0.83, 0.98] and 0.92 [0.82, 0.98] respectively. At an ‘optimal’ decision threshold, sensitivity was 0.73 [0.59, 0.85] and specificity 0.76 [0.62, 0.88]. These results characterize SVM classification repeatability for radiomic features extracted from FFDM images.