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 预计 2021 年美国将有 76,080 名新患者患肾癌，其中 13,780 人死亡。 细胞癌（RCC）是最常见的肾癌类型，造成巨大的经济负担 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，总体医疗保健 每个 RCC 患者的费用为 23,489 美元，加权总经济负担为 21 亿美元。 RCC 经常出现 作为偶然检测到的、不完全表征的肾脏肿块。其中许多患者患有偶发性肾病 肿块可以直接手术或活检，无需进一步影像学评估作为准确的组织学评估 使用当前的成像技术并不总是能够进行诊断。然而，前期手术或活检并不 理想的选择，因为近 25% 的偶然肾脏肿块要么是良性（血管平滑肌脂肪瘤、嗜酸细胞瘤），要么是低度恶性 （嫌色细胞肾细胞癌、低度透明细胞肾细胞癌）和此类肿块的过度治疗会增加不必要的风险 发病率和医疗保健费用。先前的研究表明，低级别肾细胞癌可以通过以下方法保守治疗： 对特定患者（老年患者和不适合手术的患者）进行主动监测，但 目前还没有一种非侵入性方法可以将低级别 RCC 与侵袭性 RCC（高级别透明细胞）区分开来。 RCC，乳头状肾细胞癌）。因此，迫切需要开发新型非侵入性定量方法 用于准确表征肾脏肿块的生物标志物，以便更多患者有资格接受主动监测 可以被识别。最近的研究表明，MR 组织弛豫映射包括 T1、T2 和 T2* 绘图和脂肪分数量化可以改善肾脏疾病的特征并关联 与 RCC 的肿瘤分级和生物学侵袭性相关。然而，目前的肾脏松弛测量图 技术仍然受到长时间屏气、有限的空间分辨率/覆盖范围以及大部分捕捉能力的影响 一次一种组织属性。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。在本研究中，我们建议利用新颖的 MR 指纹识别 (MRF) 技术与机器学习方法相结合，以减轻肾脏成像的上述局限性。在 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 T1、T2、T2* 和脂肪 分数定量（目标 1）。我们将把肾脏 MRF 采集与新颖的深度学习结合起来 加速数据采集和提高组织绘图效率的方法（目标 2）。最后，我们将申请 MRF 技术在 RCC 患者中的应用，探索其在表征肾癌方面的诊断能力（目的 3）。开发成功后，通过 MRF 获得的多参数定量测量可以使 MRI 是诊断和预测 RCC 肿瘤分级的更强大工具，最终目标是 消除对符合条件的良性/低级别肾细胞癌患者不必要的活检/手术并提供指导 制定最合适的治疗策略。