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: 10531904
• 负责人: Shanshan Jiang
• 金额: $ 35.97万
• 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/10531904
• 关键词: Adult; Aftercare; Amides; Angiogenesis Inhibitors; Biopsy; Biopsy Specimen; Brain Neoplasms; Caring; Chemicals; Clinical; Clinical Management; Clinical Pathways; Clinical Trials; Data; Diagnostic; Disease Progression; Early identification; Excision; FDA approved; Glioblastoma; Goals; Guidelines; Human; Image; Imaging Device; Investigational Therapies; Local Therapy; Localized Malignant Neoplasm; Magnetic Resonance Imaging; Malignant Glioma; Malignant Neoplasms; Maps; Methodology; Methods; 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; 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; 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.

摘要 尽管在治疗方面取得了进展，但最具侵袭性的脑瘤--胶质母细胞瘤--仍然几乎 普遍是致命的。这种毁灭性的癌症的一线治疗方法是最大限度地进行可行的手术切除， 放疗后配合替莫唑胺化疗(CRT)。令人鼓舞的是，有 临床试验中可改善生活质量或延长生存的多种二线疗法，如抗肿瘤药物 血管生成治疗(AAT)。在这种情况下，准确确定患者是应答者还是应答者 CRT术后早期无反应者已成为临床实践中的一个重要因素。然而， 神经成像的局限性使患者的临床管理复杂化，并阻碍了对 新疗法。即使在先进成像模式的改进下，区分真实的进展 VS假进展(由CRT引起)，或应答VS假反应(由AAT引起)保持两个 最令人敬畏的诊断难题。因此，目前诊断和局部治疗的黄金标准 计划仍然是基于对组织样本的病理评估。然而，即使这样也会产生可变的结果 肿瘤内治疗反应的异质性。因此，可靠的成像工具，能够及早 预测肿瘤对临床治疗的反应，是迫切需要的。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)的分子磁共振成像技术， 已被证明对神经肿瘤学的临床MRI评估具有重要价值。然而， 目前使用的大多数成像方案基本上是半定量的，所获得的图像通常是 因为有其他贡献，所以被称为APTw图像。值得注意的是，已经表明定量CEST-MRI是 能够在脑肿瘤患者中获得更纯净和更高的APT信号。另一方面，深深地- 学习是一种最先进的成像分析技术，它以最少的人力提供令人兴奋的解决方案 输入。具体地说，所导出的显著图充当类别区分区域的定位器，并且可以 在指导活组织检查和局部治疗方案方面具有巨大潜力。这项提案的目标是证明 定量CEST-MRI有可能解决GBM患者的两大诊断难题和 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 应用有三个具体目标：(1)实施和优化定量CEST-MRI技术和 量化其在预测CRT早期反应和生存方面的准确性；(2)确定 定量CEST-MRI以评估对贝伐单抗的反应；以及(3)开发深度学习管道， 包括用于反应性区分和立体定向活检指导的结构和CEST图像。如果 如果成功，我们的成果--特别是建立的深度学习平台--将随时可用 准确识别早期反应，指导立体定向活检，从而改变临床路径。