We propose an algorithm to recognize breast parenchyma regions containing mass-like abnormalities in dedicated breast CT images using texture feature descriptors. From 53 patient breast CT scans (29 of which containing masses), we first isolated the parenchyma through automatic segmentation, and we obtained a total of 14,751 normal 2D image patches (negatives), and 2,100 containing a breast mass (positives). We extracted 141 texture features (10 first-order descriptors, 6 Haralick features, 20 run-length features, 45 structural and pattern descriptors, 60 Gabor features), which we then analyzed through multivariate analysis of variance (MANOVA) and linear discriminant analysis, resulting in an area under the ROC curve (AUC) of 0.9. We finally identified the most discriminant features through sequential forward selection, and used them to train and validate a neural network by dividing the data into multiple batches, with each batch always containing the whole set of positive cases, and as many different negative examples. To avoid the possible bias due to the high skewness in class proportion, the training was performed on all these batches independently, without re-initializing the network weights after each training. The network was tested using an additional independent 18 patient breast CT scans (8 normal and 10 containing a mass), on a total of 7,274 image patches (852 positives, 6,422 negatives) which were not used during the training/validation phase, resulting in 95.6% precision, 95.8% recall, and 0.99 AUC. Our results suggest that the proposed approach could be further evaluated and expanded for computer-aided detection tasks in breast CT imaging.