Radiological-pathological (Rad-Path) correlation has recently allowed the validation of prostate cancer imaging technology^1,2. Multiparametric MRI (MP-MRI) has shown to be highly accurate at locating targets for biopsy³. However, difficulties still remain in this method of prostate cancer detection and grading^4,5. We present a new technique that combines the two cross-modal datasets of MP-MRI and the gold standard, pathological grading, with machine learning to generate predictive maps of histological features. These maps can be used to predict prostate cancer presence with non-invasive imaging alone.Between October of 2014 and September of 2015, twenty-six patients undergoing prostatectomy were imaged <2 weeks pre-op using a 3T MRI machine (GE) and MP-MRI. This included diffusion imaging with 10 b-values, dynamic contrast enhanced (DCE) and T2 weighted imaging. We designed and used custom 3D-printed patient-specific slicing molds to section the prostates in the same orientation as the imaging was acquired^6,7. Whole-mount sections were paraffin-embedded, H&E stained, digitized, and contoured using custom code developed in Matlab (Mathworks Inc). Segmentation features included lumen, stroma, and epithelium/nuclei (Figure 1). Digitized histology was co-registered to the T2-weighted MRI using Matlab software and non-linear warping (Figure 2). A partial least squares regression (PLS) machine-learning algorithm was trained with co-localized MRI voxel values and corresponding histology features. A leave one out approach was used to generate predictive maps of each of the three histological features (lumen, stroma, and epithelium/nuclei) for each patient using the histology and corresponding MRI values from all other patients (Figure 3). Maps of each feature were then compared to Gleason (G) graded whole mount histology, performed by a board-certified pathologist.All 26 patients had benign/atrophic glands and areas of G3 cancer. Twenty patients had regions of G4 and three patients had G5 nodules. Quantitative MRI-derived PLS trained maps of epithelium/nuclei were more predictive of the location of cancer than those of stroma and lumen (Figure 4). In 6 of 6 patients with G3 alone, the epithelium density maps indicated high percentage regions that co-localized with the pathologically confirmed G3 cancer. Likewise, in 17 of 20 G4 cases and all three patients with G5 cancer, abnormally high epithelium percentages co-localized with the pathologically confirmed regions (Figure 4).We generated machine learning trained maps of histological features based on non-invasive quantitative MR imaging. We found that locations with high epithelium/nuclei density indicated regions of G3+ cancer. More research is necessary to determine how this new machine learning based biomarker should be used for diagnostic purposes.