We investigate the task of breast tumor classification based on dynamic contrast-enhanced magnetic resonance image data (DCE-MRI). Our objective is to study how the formation of regions of similar voxels contributes to distinguishing between benign and malignant tumors. First, we perform clustering on each tumor with different algorithms and parameter settings, and then combine the clustering results to identify the most suspect region of the tumor and derive features from it. With these features we train classifiers on a set of tumors that are difficult to classify, even for human experts. We show that the features of the most suspect region alone cannot distinguish between benign and malignant tumors, yet the properties of this region are indicative of tumor malignancy for the dataset we studied.