Development of Magnetic Resonance Fingerprinting in Kidney for Evaluation of Renal Cell Carcinoma

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 and provide guidance
towards the most appropriate treatment strategy.

Grant Number: 5R01CA266702-04
NIH Institute/Center: NIH
Principal Investigator: Yong Chen