Multi-parametric Perfusion MRI for Therapy Response Assessment in Brain Cancer

多参数灌注 MRI 用于脑癌治疗反应评估

• 批准号: 10190871
• 负责人: Ashley M Stokes
• 金额: $ 34.77万
• 依托单位: ST. JOSEPH'S HOSPITAL AND MEDICAL CENTER
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10190871
• 关键词: 3-Dimensional; Adopted; Adoption; Affect; Aftercare; Agreement; Algorithmic Analysis; Algorithms; Architecture; Biological Markers; Biometry; Biopsy; Blood Vessels; Brain Neoplasms; Cellularity; Characteristics; Clinical; Clinical Management; Clinical Trials; Community Clinical Oncology Program; Computer software; Contrast Media; Data; Diagnosis; Dose; Ensure; Evaluation; Excision; Functional Magnetic Resonance Imaging; Functional disorder; Future; Gadolinium; Glioma; Goals; Guidelines; Healthcare; Image; Image Analysis; Industry Standard; Injections; Institution; Lead; Libraries; Magnetic Resonance Imaging; Malignant neoplasm of brain; Measurement; Measures; Medical Care Costs; Meta-Analysis; Methodology; Methods; Morbidity - disease rate; Multi-Institutional Clinical Trial; Multicenter Trials; Operative Surgical Procedures; Outcome; Oxygen; Patient Care; Patients; Performance; Perfusion; Permeability; Phase; Physiologic pulse; Physiological; Predisposition; Prospective cohort; Protocols documentation; Publishing; Pythons; Radiation therapy; Recurrence; Reporting; Reproducibility; Reproducibility of Results; Research; Resources; Scanning; Scheme; Site; Standardization; Structure; Testing; The Cancer Imaging Archive; Time; Vendor; automated algorithm; base; bevacizumab; cerebral blood volume; clinical practice; clinical translation; commercialization; contrast enhanced; conventional therapy; drug discovery; early detection biomarkers; healthy volunteer; imaging modality; improved; mortality; neuro-oncology; novel; open source; personalized care; predictive tools; programs; prospective; quality assurance; quantitative imaging; radiation effect; response; targeted treatment; tool; treatment planning; treatment response; tumor; tumor progression; user-friendly

The long-term goal of this program is to improve patient care by optimizing and validating quantitative magnetic resonance imaging methods for the early prediction of brain cancer response to therapy. Currently, contrast-enhanced MRI (CE-MRI) represents the standard for guiding almost all aspects of brain tumor clinical management, including surgical biopsy/resection, radiation treatment planning, and post-treatment surveillance for response assessment. Unfortunately, CE-MRI’s accuracy remains limited, which creates significant clinical challenges. Thus, clinical decisions often require surgical biopsy for definitive diagnosis, which increases medical costs, patient morbidity/mortality, and resource utilization. To overcome the limitations of CE-MRI, dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are increasingly used to evaluate tumor perfusion and permeability. Studies have shown that DSC/DCE parameters correlate with tumor grade, can predict the likelihood of tumor progression after therapy, and differentiate treatment related effects versus tumor progression. However, the widespread clinical adoption and incorporation of DSC-MRI into multi-site clinical trials has been hindered due to variable acquisition methods, contrast agent dosing schemes and analysis protocols, which to date, have yet to be standardized and automated for clinical use. These issues are known to affect the repeatability and interpretation of DSC-MRI metrics. Spin and gradient echo (SAGE) DSC-MRI sequences enable the use of lower doses of Gd-based contrast agents, require less scan time, are less sensitive to acquisition parameters, are methodologically more reproducible, yield more accurate perfusion parameters, provide simultaneous measures of DCE-MRI, vessel size and vessel architectural imaging data, oxygen delivery and novel metrics highly sensitive to tumor cellular characteristics. Accordingly, SAGE methods enable the interrogation of unique and complementary readouts on tumor microstructure and function that correlate with clinical outcomes and can identify patients responding to therapy. Before clinical trials can benefit from SAGE based DSC-MRI the acquisition and analysis protocols need to be optimized, automated and standardized. Thus, we propose to: 1) implement multi-vendor, SAGE- DSC-MRI protocols, 2) establish automated and open source algorithms for quality assurance and analysis, 3) partner with Imaging Biometrics to develop a commercially integrated, vendor neutral image analysis platform for analyzing SAGE DSC-MRI data and 4) validate SAGE DSC-MRI tools for predicting glioma response to bevacizumab therapy. Impact on Healthcare: We will provide the neuro-oncology community with validated, quantitative image acquisition and analysis methods for identifying early therapeutic response that are appropriate for multi-site clinical trials of conventional and targeted brain tumor therapies, thereby enabling more rapid drug discovery and improved individualized care for patients.

该计划的长期目标是通过优化和验证定量来改善患者护理 磁共振成像方法，用于早期预测脑癌对治疗的反应。现在， 对比增强的MRI（CE-MRI）代表了指导脑肿瘤临床几乎所有方面的标准 管理，包括手术活检/切除，放射治疗计划和治疗后监视 用于响应评估。不幸的是，CE-MRI的准确性仍然有限，这产生了重要的临床 挑战。这是临床决策通常需要进行手术活检才能确定诊断，这会增加 医疗费用，患者发病率/死亡率和资源利用。为了克服CE-MRI的局限性， 动态敏感性对比度（DSC）MRI和动态对比度增强（DCE）MRI越来越多地使用 评估肿瘤灌注和渗透性。研究表明，DSC/DCE参数与 肿瘤等级可以预测治疗后肿瘤进展的可能性，并区分相关治疗 影响与肿瘤进展。但是，DSC-MRI的临床领养和公司的宽度 由于获取方法的变化，对比剂量给予多站点临床试验已受到阻碍 迄今为止，该方案和分析方案尚未标准化和自动化以供临床使用。 已知这些问题会影响DSC-MRI指标的可重复性和解释。自旋和梯度 Echo（Sage）DSC-MRI序列可以使用较低剂量的基于GD的对比剂，需要更少 扫描时间，对采集参数不太敏感，在方法论上更可重复，产生更多 精确的灌注参数，提供简单的DCE-MRI，容器尺寸和容器 建筑成像数据，氧递送和新型指标对肿瘤细胞特征高度敏感。 彼此之间，鼠尾草方法可以对肿瘤进行独特而完整的读数进行询问 与临床结果相关的微观结构和功能，可以识别患者对 治疗。在临床试验可以从基于SAGE的DSC-MRI中受益之前，获取和分析方案 需要优化，自动化和标准化。那就是我们建议：1）实施多供应商，贤者 - DSC-MRI协议，2）建立用于质量保证和分析的自动化和开源算法，3） 与成像生物识别技术合作开发商业整合的供应商中性图像分析平台 用于分析SAGE DSC-MRI数据和4）验证SAGE DSC-MRI工具，以预测神经胶质瘤对 贝伐单抗治疗。对医疗保健的影响：我们将为神经肿瘤学社区提供经过验证的， 定量图像获取和分析方法，用于识别早期治疗反应 适用于常规和靶向脑肿瘤疗法的多部位临床试验，从而实现 更快的药物发现并改善了患者的个性化护理。