Quantitative CEST MRI for GBM Early Response Prediction and Biopsy Guidance

用于 GBM 早期反应预测和活检指导的定量 CEST MRI

• 批准号: 10319165
• 负责人: Shanshan Jiang
• 金额: $ 36.7万
• 依托单位: JOHNS HOPKINS UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-12-15 至 2025-11-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10319165
• 关键词: Adult; Aftercare; Amides; Biopsy; Biopsy Specimen; Brain Neoplasms; Caring; Chemicals; Clinical; Clinical Management; Clinical Pathways; Clinical Trials; Data; Diagnostic; Disease Progression; Excision; FDA approved; Glioblastoma; Goals; Gold; Guidelines; Human; Image; Imaging Device; Investigational Therapies; Local Therapy; Localized Malignant Neoplasm; Magnetic Resonance Imaging; Malignant Glioma; Malignant Neoplasms; Maps; Methodology; Molecular; Operative Surgical Procedures; Output; Pathologic; Pathology; Patients; Positioning Attribute; Primary Brain Neoplasms; Proteins; Protocols documentation; Protons; Quality of life; Radiation therapy; Recurrence; Recurrent tumor; Repeat Surgery; Signal Transduction; Surrogate Markers; Techniques; Testing; Tissue Sample; Treatment Protocols; base; bevacizumab; chemotherapy; clinical practice; deep learning; deep learning algorithm; diagnosis standard; efficacy evaluation; imaging modality; improved; in vivo; neuro-oncology; neuroimaging; novel diagnostics; novel therapeutics; predicting response; quantitative imaging; radiomics; recruit; response; temozolomide; treatment effect; treatment planning; treatment response; tumor; tumor diagnosis; tumor heterogeneity

ABSTRACT Despite advances in therapy, the most aggressive form of brain tumor, glioblastoma, remains almost universally fatal. The first-line therapy for this devastating cancer is maximum feasible surgical resection, followed by radiotherapy with concurrent temozolomide chemotherapy (CRT). It is encouraging that there are multiple second-line therapies in clinical trials that could improve life quality or prolong survival, such as anti- angiogenic therapy (AAT). In this scenario, the accurate determination of whether a patient is a responder or a non-responder at an early stage following CRT has become a significant factor in clinical practice. However, the limitations in neuroimaging complicate the clinical management of patients and impede efficient testing of new therapeutics. Even with the improvements in advanced imaging modalities, distinguishing true progression vs. pseudoprogression (induced by CRT), or response vs. pseudoresponse (induced by AAT) remain two of the most formidable diagnostic dilemmas. Hence, the current gold standard for diagnosis and local therapy planning is still based on pathologic appraisal of tissue samples. However, even this yields variable results due to the intra-tumoral heterogeneity of treatment response. Therefore, reliable imaging tools, capable of early prediction of the tumor response to clinical therapies, are urgently needed. Amide proton transfer-weighted (APTw) imaging is a chemical exchange saturation transfer (CEST)-based molecular MRI technique, which has been demonstrated to add important value to the clinical MRI assessment in neuro-oncology. However, most currently used imaging protocols are essentially semi-quantitative, and the images obtained are often called APTw images because of other contributions. Notably, it has been shown that quantitative CEST-MRI is able to achieve more pure and higher APT signals in patients with brain tumors. On the other hand, deep- learning is a state-of-the-art imaging analysis technique that provides exciting solutions with minimum human input. In particular, the saliency maps derived act as a localizer for class-discriminative regions, and may have great potential to guide biopsies and local treatment regimens. The goals of this proposal are to demonstrate the potential of quantitative CEST-MRI to resolve two formidable diagnostic dilemmas for GBM patients and to develop an automated deep-learning framework for post-treatment surveillance and biopsy guidance. This application has three specific aims: (1) Implement and optimize the quantitative CEST-MRI technique and quantify its accuracy in predicting early response to CRT and survival; (2) Determine the capability of quantitative CEST-MRI to assess the response to bevacizumab; and (3) Develop a deep-learning pipeline that includes structural and CEST images for responsiveness differentiation and stereotactic biopsy guidance. If successful, our results—and particularly the deep-learning platform established—will be readily available to accurately identify early response and guide stereotactic biopsy, thus changing the clinical pathway.

抽象的 尽管在治疗方面取得了进步，但最具侵略性的脑肿瘤形式，胶质母细胞瘤几乎保持不变 普遍致命。这种毁灭性癌症的一线疗法是最大的可行手术切除， 然后进行放射疗法，并进行替莫酚化疗（CRT）。令人鼓舞的是 临床试验中的多种二线疗法可以提高寿命质量或延长生存率，例如 血管生成疗法（AAT）。在这种情况下，准确确定患者是响应者还是 在CRT之后的早期阶段，无反应者已成为临床实践中的重要因素。然而， 神经影像学的局限性使患者的临床管理复杂化，并妨碍对 新理论。即使改进了高级成像方式，区分真实的进展 vs.伪雌性（由CRT诱导）或响应与伪响应（由AAT诱导）保留了两个 最强大的诊断困境。因此，目前的诊断和局部疗法的黄金标准 计划仍然基于组织样本的病理评估。但是，即使这产生了可变结果 治疗反应的肿瘤内异质性。因此，可靠的成像工具，能够尽早 迫切需要预测肿瘤对临床疗法的反应。酰胺质子转移加权 （APTW）成像是一种化学交换安全转移（CEST）的分子MRI技术，它 已被证明为神经肿瘤学的临床MRI评估增加了重要价值。然而， 当前使用的大多数成像协议本质上是半定量的，并且获得的图像通常是 由于其他贡献，称为APTW图像。值得注意的是，已经表明定量cest-mri是 它可以在脑肿瘤患者中获得更纯净和更高的APT信号。另一方面，深 学习是一种最先进的成像分析技术，可为人类提供最低限度的令人兴奋的解决方案 输入。特别是，派生的显着性图是阶级歧视区域的本地化，并且可能具有 指导活检和局部治疗方案的巨大潜力。该提议的目标是证明 定量CEST-MRI解决GBM患者的两个强大诊断困境的潜力和 开发一个自动学习框架，用于治疗后监视和活检指导。这 应用程序具有三个具体目的：（1）实施并优化定量CEST-MRI技术和 量化其在预测对CRT和生存的早期反应方面的准确性； （2）确定能力 定量CEST-MRI评估对贝伐单抗的反应； （3）开发一条深度学习的管道 包括结构性和CEST图像，用于响应能力分化和立体定向活检指导。如果 成功的结果，尤其是建立的深度学习平台 - 将很容易获得 准确地确定早期反应并引导立体定向活检，从而改变临床途径。