Early detection and monitoring of genitourinary cancers, including kidney cancer, is critical to improving care. Early detection leads to more cures, and monitoring for “minimal residual disease enables informed treatment decisions. However, there are no reliable and sensitive technologies to monitor cancer at the molecular level. Through combined expertise in optimized genomic technologies, artificial intelligence, and clinical oncology, we plan to develop an AI-based model that uses urine DNA (methylation, fragmentomics, and variant detection) to identify cancer. The year 1 milestone will be focused on model development, and year 2 will focus on model validation using clinical samples.