Predicting breast cancer recurrence remains a critical challenge in clinical oncology. We present a machine learning framework designed to predict disease progression using histopathological whole-slide imaging. We collect a dataset of 444 images from 47 patients, with each patient contributing 10 Region of Interest (ROI) images. The ROI images are slices through the cancerous tissue at a fixed resolution and contain both cancerous and regular cells. A standard Support Vector Machine (SVM) model, trained on unedited images, serves as our baseline model. We then discuss biophysical features, such as alignment of extracellular fibers and cell density, which may be used to train enhanced machine learning models.  By comparing the enhanced models with the baseline, we may identify which morphological patterns are most predictive of cancer recurrence.