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

肾脏磁共振指纹图谱用于肾细胞癌评估的发展

• 批准号: 10522570
• 负责人: Yong Chen
• 金额: $ 46.47万
• 依托单位: CASE WESTERN RESERVE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-20 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10522570
• 关键词: 3-Dimensional; Abdomen; Acceleration; Adoption; Affect; Age; Angiomyolipoma; Benign; Biological; Biological Markers; Biopsy; Breathing; Cellularity; Cessation of life; Chromophobe Renal Cell Carcinoma; Clear cell renal cell carcinoma; Clinical; Collagen; Data; Databases; Development; Diagnosis; Diagnostic; Differential Diagnosis; Dimensions; Economic Burden; Evaluation; Excision; Exhibits; Fatty acid glycerol esters; Financial Hardship; Fingerprint; Goals; Graph; Health Care Costs; Healthcare Systems; Heterogeneity; Histologic; Histology; Image; Image Analysis; Imaging Techniques; Kidney; Kidney Diseases; Lipids; Machine Learning; Magnetic Resonance; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of kidney; Maps; Measurement; Measures; Medicare; Methodology; Methods; Morbidity - disease rate; Morphologic artifacts; Motion; Neoplasm Metastasis; Normal Range; Operative Surgical Procedures; Oxyphilic Adenoma; Papillary; Patients; Positioning Attribute; Predisposition; Procedures; Property; Psychosocial Stress; Publications; Relaxation; Renal Cell Carcinoma; Renal Mass; Renal carcinoma; Reporting; Reproducibility; Resolution; Risk; Sampling; Sensitivity and Specificity; Slice; Societies; Standardization; Techniques; Technology; Three-Dimensional Imaging; Time; Tissues; Unnecessary Surgery; Up-Regulation; Validation; base; biological heterogeneity; clinical practice; comorbidity; convolutional neural network; cost; data acquisition; deep learning; diagnostic accuracy; follow-up; healthy volunteer; imaging biomarker; imaging capabilities; improved; kidney imaging; learning strategy; lipid metabolism; machine learning method; mortality; novel; older patient; overtreatment; predictive modeling; prospective; quantitative imaging; soft tissue; tissue mapping; tool; treatment strategy; tumor

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.

抽象的 肾癌预计将在2021年在美国影响76,080例新患者。肾脏 细胞癌（RCC）是最常见的肾癌类型，造成了巨大的经济负担 关于医疗保健系统。基于Seer Medicare数据库的最新研究报告说，总医疗保健 每位RCC患者的成本为23,489美元，加权总经济负担为21亿美元。 RCC经常出现 作为偶然发现的，未完全表征的肾脏质量。其中许多偶然肾脏患者 质量要么进行直接手术或活检，而无需进一步的成像评估作为准确的组织学 使用当前成像技术的诊断并不总是可能。但是，前期手术或活检不是 理想是近25％的偶然肾脏肿块是良性（血管肌瘤，癌细胞瘤）或低度 （Chromophobe RCC，低度透明细胞RCC）和此类质量的过度治疗增加了不必要的 发病率和医疗保健成本。先前的研究表明，低级RCC可以保守管理 选定患者（老年患者和手术不良的患者）的主动监测，但在 存在没有一种非侵入性方法可以将低级RCC与侵略性RCC分开（高级透明单元 RCC，Papillary RCC）。因此，紧急需要开发新颖的非侵入性定量 生物标志物以准确表征肾脏肿块，以便更多的患者有资格进行主动监测 可以识别。最近的研究表明，包括T1，T2和T2*在内的MR组织弛豫测图映射 映射和脂肪分数定量可以改善肾脏疾病的表征并相关 在RCC中具有肿瘤等级和生物侵略性。但是，当前的肾脏松弛计图 技术仍然遭受长期呼吸的影响，空间分辨率/覆盖范围有限，并且能够大多捕获 一次一种组织特性。此外，定量措施通常容易受到运动伪像的影响 可重复性和可重复性差。在这项研究中，我们建议利用新颖的MR指纹（MRF） 技术与机器学习方法一起减轻肾脏成像中上述局限性。在 特别是，我们将开发一种新的3D自由呼吸肾MRF方法，用于同时进行T1，T2，T2*和Fat 分数定量（目标1）。我们将把这个肾脏MRF获取与新颖的深度学习结合在一起 加速数据获取并提高组织映射效率的方法（AIM 2）。最后，我们将申请 RCC患者的MRF技术探索其在表征肾癌方面的诊断强度（AIM 3）。成功开发后，使用MRF获得的多参数定量措施可能会做出 MRI是RCC中诊断和预测肿瘤等级的功能更强大的工具，其最终目标是 消除有资格的良性/低级RCC患者的不必要的活检/手术，并提供指导 采取最合适的治疗策略。