Our long-term objective is to develop deep learning techniques capable of predicting characteristics and treatment response or response to surveillance to assist clinical decision- making in renal tumors that are potential candidates for ablation therapy, biopsy, active surveillance or surgical resection. An increasing number of renal tumors are being diagnosed, due in part to incidental detection from the increased use of cross-sectional imaging. Although partial nephrectomy is still considered the primary treatment for small renal masses, percutaneous ablation is increasingly performed as a therapeutic, nephron-sparing approach. One challenge for interventional radiologists and urologists who manage these patients is selection for therapy, since the average rate of progression is slow for small renal tumors and metastasis rarely occurs. A technique that could distinguish indolent tumors from those will progress based on data from the imaging methods used to detect and delineate renal masses would enable early triage to observation versus invasive treatment. Deep learning, a type of machine learning technique which takes raw images as input, and applies many layers of transformations to calculate an output signal, has already led to breakthroughs in other areas of image recognition, and is increasingly used for medical image analysis. However, its application in the field of interventional radiology is currently limited. Furthermore, no study in the literature has applied deep learning to kidney lesion segmentation and characteristics/outcome prediction. In this project, we propose to develop novel deep learning architectures based on routine MR imaging that allow for accurate renal mass segmentation and prediction of characteristics and outcome in renal tumors. Using data from four independent cohorts, we will use our deep learning architectures to predict (1) benign versus malignant histology (2) growth rate in stage 1a renal cell carcinoma (3) SSIGN score in clear cell renal cell carcinoma and (4) clinical endpoints. We will integrate segmentation and classification into one net that suitable for clinical application. In addition, we will compare results with those of experts and traditional machine learning approaches.