Renal cancer is a source of severe mortality and morbidity, not only due to the primary malignancy but also due to loss of renal function (sometimes leading to chronic kidney disease) after partial nephrectomy. Methods to noninvasively monitor renal function and predict its robustness against this decline of function are therefore in high demand. Diffusion-weighted MRI is well poised to play this role as an adjunct to renal cancer patients' existing clinical MR workup. Our group has been at the forefront of research into advanced renal diffusion MRI contrast, including methods to separate microstructure from microcirculation (intravoxel incoherent motion (IVIM)) and assess microscopic anisotropy (diffusion tensor imaging (DTI)). A recent comprehensive approach (REFMAP) collects these contrasts jointly, allowing assessment of microstructural and microcirculation anisotropy. We propose to apply this composite dataset both to classify aggressiveness of the primary renal lesion and to assess and predict post-surgical renal function. IVIM-MRI will be performed to characterize the aggressiveness of the primary lesion. The REFMAP-MRI protocol will be used to evaluate renal cancer patients before surgery and at 1 year follow up, in comparison with standard clinical workup (measured glomerular filtration rate, proteinuria). Cross-sectional correlation will validate the markers of the REFMAP-MRI technique as probes of renal function, and those baseline values predicting which patients experience renal function decline will be identified.
The threat to patient health from kidney cancer goes beyond the cancer itself, as following removal the remaining kidneys can lose function with a likelihood that is difficult to predict. Tests that can provide insight on the risk of declining kidney function may thus have a tremendous impact in improving patient health and reducing costs. By evaluating kidney cancer patients with diffusion-weighted magnetic resonance imaging, we propose to both better understand the cancer itself and potentially predict the risk of function loss before surgery.
