Translating Hyperpolarized 13C Metabolic MRI to Predict Renal Tumor Aggressiveness

转化超极化 13C 代谢 MRI 来预测肾肿瘤的侵袭性

• 批准号: 10741013
• 负责人: Peder Eric Zufall Larson
• 金额: $ 7.48万
• 依托单位: UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10741013
• 关键词: Address; Benign; Biological Markers; Biopsy; Calibration; Cardiovascular Diseases; Cessation of life; Chronic Kidney Failure; Clinical; Clinical Protocols; Clinical Research; Data; Data Set; Detection; Diagnosis; Diagnostic; Disease; Early Diagnosis; Evaluation; Excision; Future; Glycolysis; Goals; Histology; Human; Image; Imaging Techniques; Imaging technology; Incidence; Incidental Discoveries; Indolent; Investigation; Kidney; Kidney Neoplasms; Lactate Dehydrogenase; Link; Local Therapy; Magnetic Resonance Imaging; Malignant - descriptor; Malignant Neoplasms; Malignant neoplasm of kidney; Maps; Measures; Mediating; Metabolic; Metabolism; Methods; Modeling; Motivation; Neoplasm Metastasis; Noise; Operative Surgical Procedures; Pathology; Pathway interactions; Patient Selection; Patients; Performance; Production; Prognosis; Pyruvate; Pyruvate Metabolism Pathway; RF coil; Reference Standards; Renal Cell Carcinoma; Renal carcinoma; Reproducibility; Risk; Running; Safety; Scanning; Signal Transduction; Testing; Time; Translating; Unnecessary Surgery; Validation; Visualization; Work; clinical application; cohort; cost; imaging approach; imaging biomarker; improved; innovation; kinetic model; magnetic resonance imaging biomarker; metabolic imaging; metabolomics; molecular imaging; novel; overtreatment; pre-clinical; radiomics; response; risk stratification; standard of care; surgical risk; temporal measurement; tumor; tumor metabolism

Project summary: This project aims to clinically translate hyperpolarized (HP) 13C pyruvate MRI as an innovative metabolic imaging approach for noninvasive prediction of renal tumor aggressiveness, an unmet clinical need. Our study is in direct response to PAR 19-264 that supports “The optimization, application and validation of emerging imaging or biomarker approaches targeted specifically for clinical application”, with the goals to “reduce overdiagnosis”, and “identify lethal cancers from non-lethal disease”. Our project is motivated by the rising incidence of renal tumors, largely due to the increased utilization of imaging with incidental discovery of many localized tumors. These include both benign renal tumors and malignant renal cell carcinomas (RCCs). Current imaging or biopsies cannot reliably differentiate between benign tumors, low grade RCCs, and high grade RCCs. The diagnostic ambiguity has led to an overdiagnosis of many indolent tumors which are unnecessarily treated by surgery with surgical risks, and importantly, increased risk of chronic kidney disease and associated cardiovascular disease. Notably, the increased detection of RCCs has not translated into a decrease in cancer specific death. Therefore, there is a significant unmet need for novel imaging markers that can improve the risk stratification of localized renal tumors to guide patient management. HP 13C MRI is an emerging imaging technology that allows real-time pathway-specific investigation of metabolic processes that were previously inaccessible by imaging. Our pre-clinical data in orthotopic RCC tumor models have shown that HP 13C pyruvate MRI can quantitatively map the increased pyruvate-to-lactate metabolism via the lactate dehydrogenase pathway, an imageable biomarker which is strongly linked to the presence of RCC and its aggressiveness. We have also demonstrated the feasibility of acquiring dynamic HP 13C pyruvate MRI of renal tumors in patients, with excellent metabolic contrast between tumor and normal kidney. Building upon these promising preliminary data, we now propose to investigate for the first time the value of HP 13C pyruvate MRI for risk stratifying localized renal tumors. Aim 1- we will optimize the MRI acquisition strategies for renal tumor metabolic evaluation. Aim 2- we will investigate the value of HP 13C pyruvate MRI for differentiating between benign tumors, low grade RCCs, and high grade RCCs. We will also compare HP 13C data to advanced 1H MRI and radiomics analyses, and develop multi-parametric model to assess whether it can improve the prediction. Aim 3- we will determine the repeatability of HP 13C pyruvate MRI of renal tumors, and evaluate new analysis methods to further improve the robustness of metabolism quantification. Successful completion of this project will provide the first data on the value of HP 13C pyruvate MRI in predicting renal tumor aggressiveness, and will pave the way for future larger clinical studies. HP 13C pyruvate has already been shown to be safe, and we envision the 13C metabolic imaging markers to be incorporated into a state-of-the-art multi-parametric MRI to reduce the current overdiagnosis of indolent tumors while enabling the early detection of aggressive RCCs, and help safely select patients for active surveillance.

项目概要： 该项目旨在临床上将超极化（HP）13 C丙酮酸盐MRI转化为创新的代谢成像 一种非侵入性预测肾肿瘤侵袭性的方法，这是一种未满足的临床需求。我们的研究直接 响应PAR 19-264，支持“新兴成像或 专门针对临床应用的生物标志物方法”，目标是“减少过度诊断”，以及 “从非致命性疾病中鉴别出致命性癌症”。我们的项目是由肾脏肿瘤发病率上升的动机， 这主要是由于增加了对成像的利用，偶然发现了许多局部肿瘤。这些 包括良性肾肿瘤和恶性肾细胞癌（RCC）。当前成像或活检 不能可靠地区分良性肿瘤、低级别RCC和高级别RCC。诊断 不明确性导致了许多惰性肿瘤的过度诊断，这些肿瘤不必要地通过手术治疗， 手术风险，更重要的是，慢性肾脏疾病和相关心血管疾病的风险增加。 值得注意的是，RCC检测的增加并没有转化为癌症特异性死亡的减少。因此，我们认为， 对于可以改善以下疾病的危险分层的新的成像标记物存在显著的未满足的需求： 局部肾肿瘤，以指导患者管理。HP 13 C MRI是一种新兴的成像技术， 允许对代谢过程进行实时特定途径的研究， 显像我们在原位RCC肿瘤模型中的临床前数据表明，HP 13 C丙酮酸MRI可以 通过乳酸脱氢酶途径定量绘制增加的乳酸-乳酸代谢图， 可成像的生物标志物，其与RCC的存在及其侵袭性密切相关。我们还 证明了获得患者肾肿瘤的动态HP 13 C丙酮酸盐MRI的可行性， 肿瘤和正常肾脏之间的代谢对比。基于这些有希望的初步数据，我们现在 建议首次研究HP 13 C丙酮酸盐MRI对局部肾肿瘤风险分层的价值。 目标1-我们将优化MRI采集策略，用于肾肿瘤代谢评估。目标2-我们将 探讨HP 13 C丙酮酸MRI在鉴别良性肿瘤、低级别RCC和 高等级RCC。我们还将比较HP 13 C数据与先进的1H MRI和放射组学分析， 多参数模型，以评估它是否可以提高预测。目标3-我们将确定重复性 的HP 13 C丙酮酸盐MRI肾肿瘤，并评估新的分析方法，以进一步提高鲁棒性 代谢量化。成功完成这一项目将提供有关惠普价值的第一批数据 13 C丙酮酸MRI在预测肾肿瘤侵袭性方面的应用，并将为未来更大规模的临床研究铺平道路。 HP 13 C丙酮酸盐已经被证明是安全的，我们设想13 C代谢成像标记物是 纳入最先进的多参数MRI中，以减少当前对惰性肿瘤的过度诊断 同时能够早期发现侵袭性RCC，并帮助安全地选择患者进行主动监测。