Quantitative CEST MRI for GBM Early Response Prediction and Biopsy Guidance

摘要 尽管在治疗方面取得了进展，但最具侵袭性的脑肿瘤胶质母细胞瘤仍然几乎 普遍致命。这种毁灭性癌症的一线治疗方法是最大限度地可行手术切除， 随后进行放疗并同时进行替莫唑胺化疗（CRT）。令人鼓舞的是， 临床试验中的多种二线治疗可以改善生活质量或延长生存期，如抗- 血管生成疗法（AAT）。在这种情况下，准确确定患者是应答者还是应答者是一个非常重要的问题。 CRT后早期的无应答者已成为临床实践中的重要因素。然而，在这方面， 神经成像的局限性使患者的临床管理复杂化， 新疗法即使在先进的成像模式的改进，区分真正的进展， vs.假进展（由CRT诱导），或反应与假反应（由AAT诱导）仍然是两个 最可怕的诊断难题因此，目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学评估。然而，即使这样也会产生各种结果， 治疗反应的肿瘤内异质性。因此，可靠的成像工具，能够早期 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）成像是基于化学交换饱和转移（CEST）的分子MRI技术，其 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，在这方面， 大多数当前使用的成像协议基本上是半定量的，并且所获得的图像通常是 称为APTw图像，因为其他贡献。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、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，它提供了令人兴奋的解决方案， 输入.特别地，所导出的显著性图充当类别区分区域的定位器，并且可以具有 指导活检和局部治疗方案的巨大潜力。本提案的目的是证明 定量CEST-MRI解决GBM患者两个难以解决的诊断难题的潜力， 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 应用有三个具体目标：（1）实施和优化定量CEST-MRI技术， 量化其预测CRT早期反应和生存率的准确性;（2）确定 定量CEST-MRI，以评估对贝伐单抗的反应;（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引导的结构和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--将随时提供给 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。

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.