Abstract Kidney cancer is expected to affect 76,080 new patients with 13,780 deaths in the U.S. in the year 2021. Renal cell carcinoma (RCC) is the most common type of kidney cancer which imposes significant economic burden on healthcare system. A recent study based on SEER Medicare database reported that the total healthcare cost per RCC patient was $23,489 with a weighted total economic burden of $2.1 billion. RCC often presents as an incidentally detected, incompletely characterized renal mass. Many of these patients with incidental renal mass either undergo direct surgery or biopsy without further imaging evaluation as accurate histologic diagnosis with current imaging techniques is not always possible. However, upfront surgery or biopsy is not ideal as nearly 25% incidental renal masses are either benign (angiomyolipoma, oncocytoma) or low-grade (chromophobe RCC, low-grade clear cell RCC) and overtreatment of such masses adds to unnecessary morbidity and health care cost. Prior studies have shown low-grade RCC can be managed conservatively with active surveillance in select patients (elderly patients and patients who are poor surgical candidates), but at present there is a no non-invasive way to separate low-grade RCC from aggressive RCC (high-grade clear cell RCC, papillary RCC). Accordingly, there is an emergent need to develop novel non-invasive quantitative biomarkers for accurate characterization of renal masses so that more patients eligible for active surveillance could be identified. Recent studies have shown that MR tissue relaxometry mapping including T1, T2 and T2* mapping and fat fraction quantification can provide improved characterization of kidney diseases and correlate with tumor grade and biologic aggressiveness in RCC. However, the current kidney relaxometry mapping techniques still suffer from long breath-holds, limited spatial resolutions/coverage, and ability to mostly capture one tissue property at a time. Further, the quantitative measures are often susceptible to motion artifacts with poor repeatability and reproducibility. In this study, we propose to utilize the novel MR Fingerprinting (MRF) technique together with machine learning methods to mitigate aforementioned limitations in kidney imaging. In particular, we will develop a new 3D free-breathing kidney MRF method for simultaneous T1, T2, T2* and fat fraction quantification (Aim 1). We will combine this kidney MRF acquisition with novel deep learning approaches to accelerate data acquisition and improve tissue mapping efficiency (Aim 2). Finally, we will apply the MRF technique in patients with RCC to explore its diagnostic strength in characterizing kidney cancer (Aim 3). Upon successful development, the multi-parametric quantitative measures acquired with MRF could make MRI a more powerful tool for the diagnosis and predicting of tumor grade in RCC, with the ultimate goal to eliminate unnecessary biopsy/surgery in eligible patients with benign/low-grade RCCs a...