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

摘要 2021年，肾癌预计将影响美国76,080名新患者，其中13,780人死亡。肾 肾细胞癌(RCC)是最常见的肾癌类型，经济负担巨大 关于医疗保健系统。最近一项基于SEER Medicare数据库的研究报告称，总的医疗保健 每名肾癌患者的成本为23,489美元，加权总经济负担为21亿美元。RCC经常呈现 作为一种偶然发现的、不完全特征性的肾脏肿块。这些患者中的许多人患有偶发肾脏 肿块要么接受直接手术，要么接受活检，而不进行进一步的影像评估，以确定组织学是否准确 用目前的成像技术进行诊断并不总是可能的。然而，前期手术或活组织检查不是 理想的情况是近25%的偶发肾脏肿块要么是良性的(血管肌肉脂肪瘤、嗜酸细胞瘤)，要么是低级别的 (嫌色肾细胞癌，低度透明细胞肾细胞癌)和过度治疗这种肿块增加了不必要的 发病率和医疗保健费用。先前的研究表明，低级别的RCC可以通过 在选定的患者(老年患者和不适合手术的患者)中进行积极监测，但在 目前还没有一种非侵入性的方法来分离低度RCC和侵袭性RCC(高级透明细胞 肾癌、乳头状肾细胞癌)。因此，迫切需要开发新的非侵入性定量检测方法 准确描述肾脏肿块的生物标志物，以便更多的患者有资格进行积极监测 可以被辨认出来。最近的研究表明，MR组织弛豫图包括T1、T2和T2*。 作图和脂肪比例量化可以提供更好的肾脏疾病的特征和相关性 与肾癌的肿瘤分级和生物学侵袭性有关。然而，目前的肾脏松弛测绘 技术仍然受到长时间屏气、有限的空间分辨率/覆盖范围以及主要捕获能力的影响 一次只有一个组织属性。此外，定量测量通常容易受到运动伪影的影响 重复性和再现性差。在这项研究中，我们建议使用新的磁共振指纹(Mrf)。 技术与机器学习方法相结合，以减轻肾脏成像中的上述限制。在……里面 特别是，我们将开发一种新的3D自由呼吸肾脏MRF方法，用于同时检测T1、T2、T2*和FAT 分数量化(目标1)。我们将把这次肾脏MRF收购与新的深度学习结合起来 加快数据采集和提高组织测绘效率的方法(目标2)。最后，我们将申请 肾细胞癌患者磁共振血管成像技术在肾癌定性诊断中的价值 3)。在开发成功后，用磁流变液获得的多参数定量测量可以做出 MRI是诊断和预测肾细胞癌肿瘤分级的更强大的工具，最终目标是 排除符合条件的良性/低级别肾癌患者的不必要的活检/手术并提供指导 走向最合适的治疗策略。

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.