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 尽管治疗方法取得了进步，但最具侵袭性的脑肿瘤——胶质母细胞瘤仍然几乎没有被治愈。 普遍致命。这种毁灭性癌症的一线治疗方法是最大程度可行的手术切除， 随后进行放疗并同时进行替莫唑胺化疗 (CRT)。令人鼓舞的是，有 临床试验中的多种二线疗法可以改善生活质量或延长生存期，例如抗 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响应者 CRT 后早期无反应已成为临床实践中的一个重要因素。然而， 神经影像学的局限性使患者的临床管理变得复杂，并阻碍了有效的检测 新疗法。即使先进成像方式有所改进，仍无法区分真实进展 与假性进展（由 CRT 诱导）或反应与假反应（由 AAT 诱导）仍然是其中的两个 最可怕的诊断困境。因此，当前诊断和局部治疗的金标准 规划仍然基于组织样本的病理评估。然而，即使这样也会产生不同的结果，因为 治疗反应的肿瘤内异质性。因此，可靠的成像工具能够早期 迫切需要预测肿瘤对临床治疗的反应。酰胺质子转移加权 (APTw) 成像是一种基于化学交换饱和转移 (CEST) 的分子 MRI 技术， 已被证明可以为神经肿瘤学的临床 MRI 评估增加重要价值。然而， 目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，定量 CEST-MRI 已被证明 能够在脑肿瘤患者中获得更纯净、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，可以用最少的人力提供令人兴奋的解决方案 输入。特别是，导出的显着图充当类判别区域的定位器，并且可能具有 指导活检和局部治疗方案的巨大潜力。该提案的目标是证明 定量 CEST-MRI 具有解决 GBM 患者两个棘手诊断困境的潜力 开发用于治疗后监测和活检指导的自动化深度学习框架。这 应用程序具有三个具体目标：（1）实施和优化定量 CEST-MRI 技术和 量化其预测 CRT 早期反应和生存的准确性； (2) 确定能力 定量 CEST-MRI 评估贝伐珠单抗的反应； (3) 开发一个深度学习管道 包括用于反应区分和立体定向活检指导的结构和 CEST 图像。如果 如果成功的话，我们的成果，特别是所建立的深度学习平台，将很容易获得 准确识别早期反应并指导立体定向活检，从而改变临床路径。