Intraductal carcinoma of the prostate is a subtype of prostate cancer that makes up a relatively small fraction of total prostate cancer cases, but is often found in high risk or severe cases. Unfortunately, our current understanding of intraductal carcinoma of the prostate is limited due to challenges with identification using conventional methods. Currently, prostate cancer is graded based on images of small sections of the tumor. Recent advances in deep learning have enhanced our ability to detect specific cancer subtypes from images. Further, state of the art physics-based simulations provide insight into how tumors form. In this project, we will merge these two approaches to enhance the precision of prostate cancer diagnostics and study the features that underpin intraductal carcinoma of the prostate.