We present a machine learning technique for mapping prostate cancer cellular features into MRI space. 39 patients were prospectively recruited for imaging prior to prostaectomy. Tissue was aligned with the MRI using a non-linear control point warping technique. Pathologist annotations were likewise transformed into MRI space. A partial least squares regression (PLS) algorithm was trained on two sets of 10 patients and applied to 19 test patients, using MRI values as the input to predict epithelial and lumen density. The output maps are new interpretable image contrasts predictive of prostate cancer presence.